메뉴 건너뛰기




Volumn 3, Issue , 2016, Pages 257-295

Functional Data Analysis

Author keywords

Clustering and classification; Functional additive model; Functional correlation; Functional linear regression; Functional principal component analysis; Time warping

Indexed keywords


EID: 84973376001     PISSN: 23268298     EISSN: 2326831X     Source Type: Journal    
DOI: 10.1146/annurev-statistics-041715-033624     Document Type: Review
Times cited : (786)

References (218)
  • 2
    • 84873374810 scopus 로고    scopus 로고
    • Sampled forms of functional PCA in reproducing kernel Hilbert spaces
    • Amini AA, Wainwright MJ. 2012. Sampled forms of functional PCA in reproducing kernel Hilbert spaces. Ann. Stat. 40:2483-510
    • (2012) Ann. Stat. , vol.40 , pp. 2483-2510
    • Amini, A.A.1    Wainwright, M.J.2
  • 3
    • 84858177485 scopus 로고    scopus 로고
    • Clustering time-course microarray data using functional Bayesian infinite mixture model
    • AngeliniC, De Canditiis D, Pensky M. 2012. Clustering time-course microarray data using functional Bayesian infinite mixture model. J. Appl. Stat. 39:129-49
    • (2012) J. Appl. Stat. , vol.39 , pp. 129-149
    • Angelini, C.1    De Canditiis, D.2    Pensky, M.3
  • 7
    • 84863027970 scopus 로고    scopus 로고
    • Robust functional principal components: A projection-pursuit approach
    • Bali JL, Boente G, Tyler DE, Wang JL. 2011. Robust functional principal components: a projection-pursuit approach. Ann. Stat. 39:2852-82
    • (2011) Ann. Stat. , vol.39 , pp. 2852-2882
    • Bali, J.L.1    Boente, G.2    Tyler, D.E.3    Wang, J.L.4
  • 8
    • 0027453616 scopus 로고
    • Model-based Gaussian and non-Gaussian clustering
    • Banfield JD, Raftery AE. 1993. Model-based Gaussian and non-Gaussian clustering. Biometrics 49:803-21
    • (1993) Biometrics , vol.49 , pp. 803-821
    • Banfield, J.D.1    Raftery, A.E.2
  • 9
    • 0001181674 scopus 로고
    • Principal components analysis of sampled functions
    • Besse P, Ramsay JO. 1986. Principal components analysis of sampled functions. Psychometrika 51:285-311
    • (1986) Psychometrika , vol.51 , pp. 285-311
    • Besse, P.1    Ramsay, J.O.2
  • 10
    • 56449109491 scopus 로고    scopus 로고
    • Local polynomial regression on unknownmanifolds
    • ed. R Liu, WStrawderman, C-H Zhang Beachwood, OH: Inst Math. Stat.
    • Bickel P, Li B. 2007. Local polynomial regression on unknownmanifolds. In Complex Datasets and Inverse Problems: Tomography, Networks and Beyond, ed. R Liu, WStrawderman, C-H Zhang, pp. 177-86. Beachwood, OH: Inst Math. Stat.
    • (2007) Complex Datasets and Inverse Problems: Tomography, Networks and beyond , pp. 177-186
    • Bickel, P.1    Li, B.2
  • 11
    • 0000797991 scopus 로고
    • On some global measures of the deviations of density function estimates
    • Bickel PJ, RosenblattM. 1973. On some global measures of the deviations of density function estimates. Ann. Stat. 1:1071-95
    • (1973) Ann. Stat. , vol.1 , pp. 1071-1095
    • Bickel, P.J.1    Rosenblatt, M.2
  • 12
    • 0010741912 scopus 로고    scopus 로고
    • Kernel-based functional principal components
    • Boente G, Fraiman R. 2000. Kernel-based functional principal components. Stat. Probab. Lett. 48:335-45
    • (2000) Stat. Probab. Lett. , vol.48 , pp. 335-345
    • Boente, G.1    Fraiman, R.2
  • 14
    • 84946962840 scopus 로고    scopus 로고
    • S-estimators for functional principal component analysis
    • Boente G, Salibián-Barrera M. 2014. S-estimators for functional principal component analysis. J. Am. Stat. Assoc. 110:1100-11
    • (2014) J. Am. Stat. Assoc. , vol.110 , pp. 1100-1111
    • Boente, G.1    Salibián-Barrera, M.2
  • 16
    • 0032356568 scopus 로고    scopus 로고
    • Smoothing spline models for the analysis of nested and crossed samples of curves
    • Brumback B, Rice J. 1998. Smoothing spline models for the analysis of nested and crossed samples of curves. J. Am. Stat. Assoc. 93:961-76
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 961-976
    • Brumback, B.1    Rice, J.2
  • 17
    • 33847349358 scopus 로고    scopus 로고
    • Prediction in functional linear regression
    • Cai T, Hall P. 2006. Prediction in functional linear regression. Ann. Stat. 34:2159-79
    • (2006) Ann. Stat. , vol.34 , pp. 2159-2179
    • Cai, T.1    Hall, P.2
  • 18
    • 82655181491 scopus 로고    scopus 로고
    • Optimal estimation of the mean function based on discretely sampled functional data: Phase transition
    • Cai TT, Yuan M. 2011. Optimal estimation of the mean function based on discretely sampled functional data: phase transition. Ann. Stat. 39:2330-55
    • (2011) Ann. Stat. , vol.39 , pp. 2330-2355
    • Cai, T.T.1    Yuan, M.2
  • 19
    • 84860816894 scopus 로고    scopus 로고
    • Simultaneous inference for the mean function based on dense functional data
    • Cao G, Yang L, Todem D. 2012. Simultaneous inference for the mean function based on dense functional data. J. Nonparametric Stat. 24:359-77
    • (2012) J. Nonparametric Stat. , vol.24 , pp. 359-377
    • Cao, G.1    Yang, L.2    Todem, D.3
  • 20
    • 0001469917 scopus 로고    scopus 로고
    • Nonparametric estimation of smoothed principal components analysis of sampled noisy functions
    • Cardot H. 2000. Nonparametric estimation of smoothed principal components analysis of sampled noisy functions. J. Nonparametric Stat. 12:503-38
    • (2000) J. Nonparametric Stat. , vol.12 , pp. 503-538
    • Cardot, H.1
  • 21
    • 34547277358 scopus 로고    scopus 로고
    • Conditional functional principal components analysis
    • Cardot H. 2007. Conditional functional principal components analysis. Scand. J. Stat. 34:317-35
    • (2007) Scand. J. Stat. , vol.34 , pp. 317-335
    • Cardot, H.1
  • 23
    • 0041459525 scopus 로고    scopus 로고
    • Spline estimators for the functional linear model
    • Cardot H, Ferraty F, Sarda P. 2003. Spline estimators for the functional linear model. Stat. Sin. 13:571-92
    • (2003) Stat. Sin. , vol.13 , pp. 571-592
    • Cardot, H.1    Ferraty, F.2    Sarda, P.3
  • 24
    • 6444240715 scopus 로고    scopus 로고
    • Estimation in generalized linear models for functional data via penalized likelihood
    • Cardot H, Sarda P. 2005. Estimation in generalized linear models for functional data via penalized likelihood. J. Multivar. Anal. 92:24-41
    • (2005) J. Multivar. Anal. , vol.92 , pp. 24-41
    • Cardot, H.1    Sarda, P.2
  • 25
    • 84879165859 scopus 로고    scopus 로고
    • Linear regression models for functional data
    • ed. S Sperlich, G Aydinli Heidelberg, Ger. : Springer
    • Cardot H, Sarda P. 2006. Linear regression models for functional data. In The Art of Semiparametrics, ed. S Sperlich, G Aydinli, pp. 49-66. Heidelberg, Ger. : Springer
    • (2006) Art of Semiparametrics , pp. 49-66
    • Cardot, H.1    Sarda, P.2
  • 26
    • 66249142283 scopus 로고    scopus 로고
    • Nonparametric additive regression for repeatedly measured data
    • Carroll RJ, Maity A, Mammen E, Yu K. 2009. Nonparametric additive regression for repeatedly measured data. Biometrika 96:383-98
    • (2009) Biometrika , vol.96 , pp. 383-398
    • Carroll, R.J.1    Maity, A.2    Mammen, E.3    Yu, K.4
  • 27
    • 0022806525 scopus 로고
    • Principal modes of variation for processes with continuous sample curves
    • Castro PE, Lawton WH, Sylvestre EA. 1986. Principal modes of variation for processes with continuous sample curves. Technometrics 28:329-37
    • (1986) Technometrics , vol.28 , pp. 329-337
    • Castro, P.E.1    Lawton, W.H.2    Sylvestre, E.A.3
  • 28
    • 84911992679 scopus 로고    scopus 로고
    • Functional data classification: A wavelet approach
    • ChangC, Chen Y, Ogden RT. 2014. Functional data classification: a wavelet approach. Comput. Stat. 29:1497-1513
    • (2014) Comput. Stat. , vol.29 , pp. 1497-1513
    • Chang, C.1    Chen, Y.2    Ogden, R.T.3
  • 29
    • 84860885088 scopus 로고    scopus 로고
    • Single andmultiple index functional regression models with nonparametric link
    • Chen D, Hall P, MüllerHG. 2011. Single andmultiple index functional regression models with nonparametric link. Ann. Stat. 39:1720-47
    • (2011) Ann. Stat. , vol.39 , pp. 1720-1747
    • Chen, D.1    Hall, P.2    Müller, H.G.3
  • 30
    • 84871206749 scopus 로고    scopus 로고
    • Nonlinear manifold representations for functional data
    • Chen D, Müller HG. 2012. Nonlinear manifold representations for functional data. Ann. Stat. 40:1-29
    • (2012) Ann. Stat. , vol.40 , pp. 1-29
    • Chen, D.1    Müller, H.G.2
  • 31
  • 32
    • 84901049442 scopus 로고    scopus 로고
    • Dynamical functional prediction and classification, with application to traffic flow prediction
    • Chiou JM. 2012. Dynamical functional prediction and classification, with application to traffic flow prediction. Ann. Appl. Stat. 6:1588-614
    • (2012) Ann. Appl. Stat. , vol.6 , pp. 1588-1614
    • Chiou, J.M.1
  • 33
    • 34547837451 scopus 로고    scopus 로고
    • Functional clustering and identifying substructures of longitudinal data
    • Chiou JM, Li PL. 2007. Functional clustering and identifying substructures of longitudinal data. J. R. Stat. Soc. Ser. B 69:679-99
    • (2007) J. R. Stat. Soc. Ser. B , vol.69 , pp. 679-699
    • Chiou, J.M.1    Li, P.L.2
  • 34
    • 77949530699 scopus 로고    scopus 로고
    • Correlation-based functional clustering via subspace projection
    • Chiou JM, Li PL. 2008. Correlation-based functional clustering via subspace projection. J. Am. Stat. Assoc. 103:1684-92
    • (2008) J. Am. Stat. Assoc. , vol.103 , pp. 1684-1692
    • Chiou, J.M.1    Li, P.L.2
  • 35
    • 34247400715 scopus 로고    scopus 로고
    • Diagnostics for functional regression via residual processes
    • Chiou JM, Müller HG. 2007. Diagnostics for functional regression via residual processes. Comput. Stat. Data Anal. 51:4849-63
    • (2007) Comput. Stat. Data Anal. , vol.51 , pp. 4849-4863
    • Chiou, J.M.1    Müller, H.G.2
  • 36
    • 66549091332 scopus 로고    scopus 로고
    • Modeling hazard rates as functional data for the analysis of cohort lifetables and mortality forecasting
    • Chiou JM, Müller HG. 2009. Modeling hazard rates as functional data for the analysis of cohort lifetables and mortality forecasting. J. Am. Stat. Assoc. 104:572-85
    • (2009) J. Am. Stat. Assoc. , vol.104 , pp. 572-585
    • Chiou, J.M.1    Müller, H.G.2
  • 37
    • 0038446322 scopus 로고    scopus 로고
    • Functional quasi-likelihood regression models with smooth random effects
    • Chiou JM, Müller HG, Wang JL. 2003. Functional quasi-likelihood regression models with smooth random effects. J. R. Stat. Soc. Ser. B 65:405-23
    • (2003) J. R. Stat. Soc. Ser. B , vol.65 , pp. 405-423
    • Chiou, J.M.1    Müller, H.G.2    Wang, J.L.3
  • 38
    • 84888857815 scopus 로고    scopus 로고
    • Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data
    • Coffey N, Hinde J, Holian E. 2014. Clustering longitudinal profiles using P-splines and mixed effects models applied to time-course gene expression data. Comput. Stat. Data Anal. 71:14-29
    • (2014) Comput. Stat. Data Anal. , vol.71 , pp. 14-29
    • Coffey, N.1    Hinde, J.2    Holian, E.3
  • 40
    • 77949406317 scopus 로고    scopus 로고
    • Necessary and sufficient conditions for consistency of a method for smoothed functional inverse regression
    • Cook RD, Forzani L, Yao AF. 2010. Necessary and sufficient conditions for consistency of a method for smoothed functional inverse regression. Stat. Sin. 20:235-38
    • (2010) Stat. Sin. , vol.20 , pp. 235-238
    • Cook, R.D.1    Forzani, L.2    Yao, A.F.3
  • 41
    • 52249099264 scopus 로고    scopus 로고
    • Robust nonparametric estimation for functional data
    • Crambes C, Delsol L, Laksaci A. 2008. Robust nonparametric estimation for functional data. J. Nonparametric Stat. 20:573-98
    • (2008) J. Nonparametric Stat. , vol.20 , pp. 573-598
    • Crambes, C.1    Delsol, L.2    Laksaci, A.3
  • 42
    • 84892512066 scopus 로고    scopus 로고
    • A partial overview of the theory of statistics with functional data
    • Cuevas A. 2014. A partial overview of the theory of statistics with functional data. J. Stat. Plan. Inference 147:1-23
    • (2014) J. Stat. Plan. Inference , vol.147 , pp. 1-23
    • Cuevas, A.1
  • 45
    • 0000957849 scopus 로고
    • Asymptotic theory for the principal component analysis of a vector random function: Some applications to statistical inference
    • Dauxois J, Pousse A, Romain Y. 1982. Asymptotic theory for the principal component analysis of a vector random function: some applications to statistical inference. J. Multivariate Anal. 12:136-54
    • (1982) J. Multivariate Anal. , vol.12 , pp. 136-154
    • Dauxois, J.1    Pousse, A.2    Romain, Y.3
  • 47
    • 54049101500 scopus 로고    scopus 로고
    • Asymptotics for the nonparametric estimation of the mean function of a random process
    • Degras DA. 2008. Asymptotics for the nonparametric estimation of the mean function of a random process. Stat. Probab. Lett. 78:2976-80
    • (2008) Stat. Probab. Lett. , vol.78 , pp. 2976-2980
    • Degras, D.A.1
  • 48
    • 84857141061 scopus 로고    scopus 로고
    • Simultaneous confidence bands for nonparametric regression with functional data
    • Degras DA. 2011. Simultaneous confidence bands for nonparametric regression with functional data. Stat. Sin. 21:1735-65
    • (2011) Stat. Sin. , vol.21 , pp. 1735-1765
    • Degras, D.A.1
  • 49
    • 77649330804 scopus 로고    scopus 로고
    • Defining probability density for a distribution of random functions
    • Delaigle A, Hall P. 2010. Defining probability density for a distribution of random functions. Ann. Stat. 38:1171-93
    • (2010) Ann. Stat. , vol.38 , pp. 1171-1193
    • Delaigle, A.1    Hall, P.2
  • 50
    • 84858298016 scopus 로고    scopus 로고
    • Achieving near perfect classification for functional data
    • Delaigle A, Hall P. 2012. Achieving near perfect classification for functional data. J. R. Stat. Soc. Ser. B 74:267-86
    • (2012) J. R. Stat. Soc. Ser. B , vol.74 , pp. 267-286
    • Delaigle, A.1    Hall, P.2
  • 51
    • 84901744681 scopus 로고    scopus 로고
    • Classification using censored functional data
    • Delaigle A, Hall P. 2013. Classification using censored functional data. J. Am. Stat. Assoc. 108:1269-83
    • (2013) J. Am. Stat. Assoc. , vol.108 , pp. 1269-1283
    • Delaigle, A.1    Hall, P.2
  • 52
    • 17444432924 scopus 로고    scopus 로고
    • Image manifolds which are isometric to Euclidean space
    • Donoho DL, Grimes C. 2005. Image manifolds which are isometric to Euclidean space. J. Math. Imaging Vis. 23:5-24
    • (2005) J. Math. Imaging Vis. , vol.23 , pp. 5-24
    • Donoho, D.L.1    Grimes, C.2
  • 53
    • 84873369111 scopus 로고    scopus 로고
    • Estimation in functional regression for general exponential families
    • Dou WW, Pollard D, Zhou HH. 2012. Estimation in functional regression for general exponential families. Ann. Stat. 40:2421-51
    • (2012) Ann. Stat. , vol.40 , pp. 2421-2451
    • Dou, W.W.1    Pollard, D.2    Zhou, H.H.3
  • 54
    • 0000975683 scopus 로고
    • Slicing regression: A link-free regression method
    • Duan N, Li KC. 1991. Slicing regression: a link-free regression method. Ann. Stat. 19:505-30
    • (1991) Ann. Stat. , vol.19 , pp. 505-530
    • Duan, N.1    Li, K.C.2
  • 55
    • 19744377510 scopus 로고    scopus 로고
    • Dynamical correlation for multivariate longitudinal data
    • Dubin JA, Müller HG. 2005. Dynamical correlation for multivariate longitudinal data. J. Am. Stat. Assoc. 100:872-81
    • (2005) J. Am. Stat. Assoc. , vol.100 , pp. 872-881
    • Dubin, J.A.1    Müller, H.G.2
  • 56
    • 70350391229 scopus 로고    scopus 로고
    • Convergence rates for smoothing spline estimators in varying coefficient models
    • Eggermont PPB, Eubank RL, LaRiccia VN. 2010. Convergence rates for smoothing spline estimators in varying coefficient models. J. Stat. Plann. Inference 140:369-81
    • (2010) J. Stat. Plann. Inference , vol.140 , pp. 369-381
    • Eggermont, P.P.B.1    Eubank, R.L.2    LaRiccia, V.N.3
  • 58
    • 48249103423 scopus 로고    scopus 로고
    • Canonical correlation for stochastic processes
    • Eubank RL, Hsing T. 2008. Canonical correlation for stochastic processes. Stoch. Process. Appl. 118:1634-61
    • (2008) Stoch. Process. Appl. , vol.118 , pp. 1634-1661
    • Eubank, R.L.1    Hsing, T.2
  • 60
    • 0032332076 scopus 로고    scopus 로고
    • Test of significance when data are curves
    • Fan J, Lin SK. 1998. Test of significance when data are curves. J. Am. Stat. Assoc. 93:1007-21
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 1007-1021
    • Fan, J.1    Lin, S.K.2
  • 61
    • 0033233728 scopus 로고    scopus 로고
    • Statistical estimation in varying coefficient models
    • Fan J, Zhang W. 1999. Statistical estimation in varying coefficient models. Ann. Stat. 27:1491-518
    • (1999) Ann. Stat. , vol.27 , pp. 1491-1518
    • Fan, J.1    Zhang, W.2
  • 62
    • 68149148542 scopus 로고    scopus 로고
    • Statistical methods with varying coefficient models
    • Fan J, Zhang W. 2008. Statistical methods with varying coefficient models. Stat. Interface 1:179-95
    • (2008) Stat. Interface , vol.1 , pp. 179-195
    • Fan, J.1    Zhang, W.2
  • 63
    • 84919429851 scopus 로고    scopus 로고
    • Functional response additive model estimation with online virtual stock markets
    • Fan Y, Foutz N, James GM, JankW. 2014. Functional response additive model estimation with online virtual stock markets. Ann. Appl. Stat. 8:2435-60
    • (2014) Ann. Appl. Stat. , vol.8 , pp. 2435-2460
    • Fan, Y.1    Foutz, N.2    James, G.M.3    Jank, W.4
  • 64
    • 78651323070 scopus 로고    scopus 로고
    • Most-predictive design points for functional data predictors
    • Ferraty F, Hall P, Vieu P. 2010. Most-predictive design points for functional data predictors. Biometrika 97:807-24
    • (2010) Biometrika , vol.97 , pp. 807-824
    • Ferraty, F.1    Hall, P.2    Vieu, P.3
  • 65
    • 0141989607 scopus 로고    scopus 로고
    • Curves discrimination: A nonparametric functional approach
    • Ferraty F, Vieu P. 2003. Curves discrimination: a nonparametric functional approach. Comput. Stat. Data Anal. 44:161-73
    • (2003) Comput. Stat. Data Anal. , vol.44 , pp. 161-173
    • Ferraty, F.1    Vieu, P.2
  • 67
    • 0345491393 scopus 로고    scopus 로고
    • Functional sliced inverse regression analysis
    • Ferré L, Yao AF. 2003. Functional sliced inverse regression analysis. Statistics 37:475-88
    • (2003) Statistics , vol.37 , pp. 475-488
    • Ferré, L.1    Yao, A.F.2
  • 68
    • 27144503064 scopus 로고    scopus 로고
    • Smoothed functional inverse regression
    • Ferré L, Yao AF. 2005. Smoothed functional inverse regression. Stat. Sin. 15:665-83
    • (2005) Stat. Sin. , vol.15 , pp. 665-683
    • Ferré, L.1    Yao, A.F.2
  • 69
    • 33744726447 scopus 로고    scopus 로고
    • A proposal for robust curve clustering
    • Garcia-Escudero LA, Gordaliza A. 2005. A proposal for robust curve clustering. J. Classif. 22:185-201
    • (2005) J. Classif. , vol.22 , pp. 185-201
    • Garcia-Escudero, L.A.1    Gordaliza, A.2
  • 70
    • 21344464179 scopus 로고
    • Searching for structure in curve samples
    • Gasser T, Kneip A. 1995. Searching for structure in curve samples. J. Am. Stat. Assoc. 90:1179-88
    • (1995) J. Am. Stat. Assoc. , vol.90 , pp. 1179-1188
    • Gasser, T.1    Kneip, A.2
  • 72
    • 50949086005 scopus 로고    scopus 로고
    • Robust functional estimation using the median and spherical principal components
    • Gervini D. 2008. Robust functional estimation using the median and spherical principal components. Biometrika 95:587-600
    • (2008) Biometrika , vol.95 , pp. 587-600
    • Gervini, D.1
  • 73
    • 84941568863 scopus 로고    scopus 로고
    • Warped functional regression
    • Gervini D. 2015. Warped functional regression. Biometrika 102:1-14
    • (2015) Biometrika , vol.102 , pp. 1-14
    • Gervini, D.1
  • 74
    • 84875955983 scopus 로고    scopus 로고
    • Wavelet-based clustering for mixed-effects functional models in high dimension
    • Giacofci M, Lambert-Lacroix S, Marot G, Picard F. 2013. Wavelet-based clustering for mixed-effects functional models in high dimension. Biometrics 69:31-40
    • (2013) Biometrics , vol.69 , pp. 31-40
    • Giacofci, M.1    Lambert-Lacroix, S.2    Marot, G.3    Picard, F.4
  • 75
    • 0000360828 scopus 로고
    • Stochastic processes and statistical inference
    • Grenander U. 1950. Stochastic processes and statistical inference. Arkiv Matematik 1:195-277
    • (1950) Arkiv Matematik , vol.1 , pp. 195-277
    • Grenander, U.1
  • 77
    • 84936773585 scopus 로고    scopus 로고
    • Unifying amplitude and phase analysis: A compositional data approach to functional multivariate mixed-effects modeling ofMandarin Chinese
    • Hadjipantelis PZ, Aston JA, Müller HG, Evans JP. 2015. Unifying amplitude and phase analysis: a compositional data approach to functional multivariate mixed-effects modeling ofMandarin Chinese. J. Am. Stat. Assoc. 110:545-59
    • (2015) J. Am. Stat. Assoc. , vol.110 , pp. 545-559
    • Hadjipantelis, P.Z.1    Aston, J.A.2    Müller, H.G.3    Evans, J.P.4
  • 78
    • 35348874516 scopus 로고    scopus 로고
    • Methodology and convergence rates for functional linear regression
    • Hall P, Horowitz JL. 2007. Methodology and convergence rates for functional linear regression. Ann. Stat. 35:70-91
    • (2007) Ann. Stat. , vol.35 , pp. 70-91
    • Hall, P.1    Horowitz, J.L.2
  • 79
    • 33645039219 scopus 로고    scopus 로고
    • On properties of functional principal components analysis
    • Hall P, Hosseini-Nasab M. 2006. On properties of functional principal components analysis. J. R. Stat. Soc. Ser. B 68:109-26
    • (2006) J. R. Stat. Soc. Ser. B , vol.68 , pp. 109-126
    • Hall, P.1    Hosseini-Nasab, M.2
  • 80
    • 33747153005 scopus 로고    scopus 로고
    • Properties of principal component methods for functional and longitudinal data analysis
    • Hall P, Müller HG, Wang JL. 2006. Properties of principal component methods for functional and longitudinal data analysis. Ann. Stat. 34:1493-517
    • (2006) Ann. Stat. , vol.34 , pp. 1493-1517
    • Hall, P.1    Müller, H.G.2    Wang, J.L.3
  • 81
    • 69949145955 scopus 로고    scopus 로고
    • Estimation of functional derivatives
    • Hall P, Müller HG, Yao F. 2009. Estimation of functional derivatives. Ann. Stat. 37:3307-29
    • (2009) Ann. Stat. , vol.37 , pp. 3307-3329
    • Hall, P.1    Müller, H.G.2    Yao, F.3
  • 82
    • 0035252777 scopus 로고    scopus 로고
    • A functional data analytic approach to signal discrimination
    • Hall P, PoskittDS, Presnell B. 2001. A functional data analytic approach to signal discrimination. Technometrics 43:1-9
    • (2001) Technometrics , vol.43 , pp. 1-9
    • Hall, P.1    Poskitt, D.S.2    Presnell, B.3
  • 83
    • 38549162811 scopus 로고    scopus 로고
    • Two-sample tests in functional data analysis starting from discrete data
    • Hall P, Van Keilegom I. 2007. Two-sample tests in functional data analysis starting from discrete data. Stat. Sin. 17:1511
    • (2007) Stat. Sin. , vol.17 , pp. 1511
    • Hall, P.1    Van Keilegom, I.2
  • 84
    • 84972488102 scopus 로고
    • Generalized additive models
    • Hastie T, Tibshirani R. 1986. Generalized additive models. Stat. Sci. 1:297-310
    • (1986) Stat. Sci. , vol.1 , pp. 297-310
    • Hastie, T.1    Tibshirani, R.2
  • 85
    • 0037993212 scopus 로고    scopus 로고
    • Extending correlation and regression from multivariate to functional data
    • ed. ML Puri Leiden, Neth: VSP Int.
    • He G, MüllerHG, Wang JL. 2000. Extending correlation and regression from multivariate to functional data. In Asymptotics in Statistics and Probability, ed. ML Puri, pp. 197-210. Leiden, Neth: VSP Int.
    • (2000) Asymptotics in Statistics and Probability , pp. 197-210
    • He, G.1    Müller, H.G.2    Wang, J.L.3
  • 86
    • 0038734117 scopus 로고    scopus 로고
    • Functional canonical analysis for square integrable stochastic processes
    • He G, Müller HG, Wang JL. 2003. Functional canonical analysis for square integrable stochastic processes. J. Multivariate Anal. 85:54-77
    • (2003) J. Multivariate Anal. , vol.85 , pp. 54-77
    • He, G.1    Müller, H.G.2    Wang, J.L.3
  • 87
    • 77957588988 scopus 로고    scopus 로고
    • Functional linear regression via canonical analysis
    • He G, Müller HG, Wang JL, Yang W. 2010. Functional linear regression via canonical analysis. Bernoulli 16:705-29
    • (2010) Bernoulli , vol.16 , pp. 705-729
    • He, G.1    Müller, H.G.2    Wang, J.L.3    Yang, W.4
  • 88
    • 0001558410 scopus 로고
    • Spline smoothing in a partly linear model
    • Heckman NE. 1986. Spline smoothing in a partly linear model. J. R. Stat. Soc. Ser. B 48:244-48
    • (1986) J. R. Stat. Soc. Ser. B , vol.48 , pp. 244-248
    • Heckman, N.E.1
  • 89
    • 84873948659 scopus 로고    scopus 로고
    • Clustering in linear mixed models with approximate Dirichlet process mixtures using em algorithm
    • Heinzl F, Tutz G. 2013. Clustering in linear mixed models with approximate Dirichlet process mixtures using EM algorithm. Stat. Model. 13:41-67
    • (2013) Stat. Model. , vol.13 , pp. 41-67
    • Heinzl, F.1    Tutz, G.2
  • 90
    • 84891488746 scopus 로고    scopus 로고
    • Clustering in linear-mixed models with a group fused lasso penalty
    • Heinzl F, Tutz G. 2014. Clustering in linear-mixed models with a group fused lasso penalty. Biometrical J. 56:44-68
    • (2014) Biometrical J. , vol.56 , pp. 44-68
    • Heinzl, F.1    Tutz, G.2
  • 91
    • 84879170216 scopus 로고    scopus 로고
    • Minimax adaptive tests for the functional linear model
    • Hilgert N, Mas A, Verzelen N. 2013. Minimax adaptive tests for the functional linear model. Ann. Stat. 41:838-69
    • (2013) Ann. Stat. , vol.41 , pp. 838-869
    • Hilgert, N.1    Mas, A.2    Verzelen, N.3
  • 92
    • 33746302303 scopus 로고    scopus 로고
    • Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data
    • Hoover D, Rice J, Wu C, Yang L. 1998. Nonparametric smoothing estimates of time-varying coefficient models with longitudinal data. Biometrika 85:809-22
    • (1998) Biometrika , vol.85 , pp. 809-822
    • Hoover, D.1    Rice, J.2    Wu, C.3    Yang, L.4
  • 94
    • 84888322484 scopus 로고    scopus 로고
    • A test of significance in functional quadratic regression
    • Horváth L, Reeder R. 2013. A test of significance in functional quadratic regression. Bernoulli 19:2120-51
    • (2013) Bernoulli , vol.19 , pp. 2120-2151
    • Horváth, L.1    Reeder, R.2
  • 96
    • 24944484263 scopus 로고    scopus 로고
    • Profile-kernel versus backfitting in the partially linear models for longitudinal/ clustered data
    • Hu Z, Wang N, Carroll RJ. 2004. Profile-kernel versus backfitting in the partially linear models for longitudinal/ clustered data. Biometrika 91:251-62
    • (2004) Biometrika , vol.91 , pp. 251-262
    • Hu, Z.1    Wang, N.2    Carroll, R.J.3
  • 97
    • 0011799801 scopus 로고    scopus 로고
    • Varying-coefficient models and basis function approximations for the analysis of repeated measurements
    • Huang J, Wu C, Zhou L. 2002. Varying-coefficient models and basis function approximations for the analysis of repeated measurements. Biometrika 89:111-28
    • (2002) Biometrika , vol.89 , pp. 111-128
    • Huang, J.1    Wu, C.2    Zhou, L.3
  • 98
    • 8644255231 scopus 로고    scopus 로고
    • Polynomial spline estimation and inference for varying coefficient models with longitudinal data
    • Huang J, Wu C, Zhou L. 2004. Polynomial spline estimation and inference for varying coefficient models with longitudinal data. Stat. Sin. 14:763-88
    • (2004) Stat. Sin. , vol.14 , pp. 763-788
    • Huang, J.1    Wu, C.2    Zhou, L.3
  • 99
    • 77749302275 scopus 로고    scopus 로고
    • Rainbow plots, bagplots, and boxplots for functional data
    • Hyndman RJ, Shang HL. 2010. Rainbow plots, bagplots, and boxplots for functional data. J. Comput. Graph. Stat. 19:29-45
    • (2010) J. Comput. Graph. Stat. , vol.19 , pp. 29-45
    • Hyndman, R.J.1    Shang, H.L.2
  • 101
    • 84877616133 scopus 로고    scopus 로고
    • Funclust: A curves clustering method using functional random variables density approximation
    • Jacques J, Preda C. 2013. Funclust: a curves clustering method using functional random variables density approximation. Neurocomputing 112:164-71
    • (2013) Neurocomputing , vol.112 , pp. 164-171
    • Jacques, J.1    Preda, C.2
  • 102
    • 84889093227 scopus 로고    scopus 로고
    • Model-based clustering for multivariate functional data
    • Jacques J, Preda C. 2014. Model-based clustering for multivariate functional data. Comput. Stat. Data Anal. 71:92-106
    • (2014) Comput. Stat. Data Anal. , vol.71 , pp. 92-106
    • Jacques, J.1    Preda, C.2
  • 103
    • 0036020889 scopus 로고    scopus 로고
    • Generalized linear models with functional predictors
    • James GM. 2002. Generalized linear models with functional predictors. J. R. Stat. Soc. Ser. B 64:411-32
    • (2002) J. R. Stat. Soc. Ser. B , vol.64 , pp. 411-432
    • James, G.M.1
  • 104
    • 0035535702 scopus 로고    scopus 로고
    • Functional linear discriminant analysis for irregularly sampled curves
    • James GM, Hastie TJ. 2001. Functional linear discriminant analysis for irregularly sampled curves. J. R. Stat. Soc. Ser. B 63:533-50
    • (2001) J. R. Stat. Soc. Ser. B , vol.63 , pp. 533-550
    • James, G.M.1    Hastie, T.J.2
  • 105
    • 0001699048 scopus 로고    scopus 로고
    • Principal component models for sparse functional data
    • James GM, Hastie TJ, Sugar C. 2000. Principal component models for sparse functional data. Biometrika 87:587-602
    • (2000) Biometrika , vol.87 , pp. 587-602
    • James, G.M.1    Hastie, T.J.2    Sugar, C.3
  • 106
  • 107
    • 0043245795 scopus 로고    scopus 로고
    • Clustering for sparsely sampled functional data
    • James GM, Sugar CA. 2003. Clustering for sparsely sampled functional data. J. Am. Stat. Assoc. 98:397-408
    • (2003) J. Am. Stat. Assoc. , vol.98 , pp. 397-408
    • James, G.M.1    Sugar, C.A.2
  • 108
    • 67349144705 scopus 로고    scopus 로고
    • Smoothing dynamic positron emission tomography time courses using functional principal components
    • Jiang C, Aston JA, Wang JL. 2009. Smoothing dynamic positron emission tomography time courses using functional principal components. NeuroImage 47:184-93
    • (2009) NeuroImage , vol.47 , pp. 184-193
    • Jiang, C.1    Aston, J.A.2    Wang, J.L.3
  • 109
    • 77649322081 scopus 로고    scopus 로고
    • Covariate adjusted functional principal components analysis for longitudinal data
    • Jiang C, Wang JL. 2010. Covariate adjusted functional principal components analysis for longitudinal data. Ann. Stat. 38:1194-226
    • (2010) Ann. Stat. , vol.38 , pp. 1194-1226
    • Jiang, C.1    Wang, J.L.2
  • 110
    • 79551584533 scopus 로고    scopus 로고
    • Functional single index models for longitudinal data
    • Jiang C, Wang JL. 2011. Functional single index models for longitudinal data. Ann. Stat. 39:362-88
    • (2011) Ann. Stat. , vol.39 , pp. 362-388
    • Jiang, C.1    Wang, J.L.2
  • 111
    • 84901717148 scopus 로고    scopus 로고
    • Inverse regression for longitudinal data
    • Jiang C, YuW, Wang JL. 2014. Inverse regression for longitudinal data. Ann. Stat. 42:563-91
    • (2014) Ann. Stat. , vol.42 , pp. 563-591
    • Jiang, C.1    Yu, W.2    Wang, J.L.3
  • 113
    • 84887505391 scopus 로고
    • Displaying the important features of large collections of similar curves
    • Jones MC, Rice JA. 1992. Displaying the important features of large collections of similar curves. Am. Stat. 46:140-45
    • (1992) Am. Stat. , vol.46 , pp. 140-145
    • Jones, M.C.1    Rice, J.A.2
  • 116
    • 77957020005 scopus 로고    scopus 로고
    • Functional cluster analysis via orthonormalized Gaussian basis expansions and its application
    • Kayano M, Dozono K, Konishi S. 2010. Functional cluster analysis via orthonormalized Gaussian basis expansions and its application. J. Classif. 27:211-30
    • (2010) J. Classif. , vol.27 , pp. 211-230
    • Kayano, M.1    Dozono, K.2    Konishi, S.3
  • 117
    • 0024330924 scopus 로고
    • A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters
    • Kirkpatrick M, Heckman N. 1989. A quantitative genetic model for growth, shape, reaction norms, and other infinite-dimensional characters. J. Math. Biol. 27:429-50
    • (1989) J. Math. Biol. , vol.27 , pp. 429-450
    • Kirkpatrick, M.1    Heckman, N.2
  • 118
    • 78149287743 scopus 로고
    • Principal components of random variables with values in a separable Hilbert space
    • Kleffe J. 1973. Principal components of random variables with values in a separable Hilbert space. Math. Oper. Stat. 4:391-406
    • (1973) Math. Oper. Stat. , vol.4 , pp. 391-406
    • Kleffe, J.1
  • 119
    • 0001881719 scopus 로고
    • Convergence and consistency results for self-modeling nonlinear regression
    • Kneip A, Gasser T. 1988. Convergence and consistency results for self-modeling nonlinear regression. Ann. Stat. 16:82-112
    • (1988) Ann. Stat. , vol.16 , pp. 82-112
    • Kneip, A.1    Gasser, T.2
  • 120
    • 21144472058 scopus 로고
    • Statistical tools to analyze data representing a sample of curves
    • Kneip A, Gasser T. 1992. Statistical tools to analyze data representing a sample of curves. Ann. Stat. 20:1266-305
    • (1992) Ann. Stat. , vol.20 , pp. 1266-1305
    • Kneip, A.1    Gasser, T.2
  • 121
    • 54949101671 scopus 로고    scopus 로고
    • Combining registration and fitting for functional models
    • Kneip A, Ramsay JO. 2008. Combining registration and fitting for functional models. J. Am. Stat. Assoc. 103:1155-65
    • (2008) J. Am. Stat. Assoc. , vol.103 , pp. 1155-1165
    • Kneip, A.1    Ramsay, J.O.2
  • 122
    • 82655189987 scopus 로고    scopus 로고
    • Factor models and variable selection in high-dimensional regression analysis
    • Kneip A, Sarda P. 2011. Factor models and variable selection in high-dimensional regression analysis. Ann. Stat. 39:2410-47
    • (2011) Ann. Stat. , vol.39 , pp. 2410-2447
    • Kneip, A.1    Sarda, P.2
  • 123
    • 1542678851 scopus 로고    scopus 로고
    • Inference for density families using functional principal component analysis
    • Kneip A, Utikal KJ. 2001. Inference for density families using functional principal component analysis. J. Am. Stat. Assoc. 96:519-42
    • (2001) J. Am. Stat. Assoc. , vol.96 , pp. 519-542
    • Kneip, A.1    Utikal, K.J.2
  • 124
    • 84960090412 scopus 로고    scopus 로고
    • Partially functional linear regression in high dimensions
    • Kong D, Xue K, Yao F, Zhang HH. 2015. Partially functional linear regression in high dimensions. Biometrika 103:147-59
    • (2015) Biometrika , vol.103 , pp. 147-159
    • Kong, D.1    Xue, K.2    Yao, F.3    Zhang, H.H.4
  • 125
    • 84869404935 scopus 로고    scopus 로고
    • Dispersion operators and resistant second-order functional data analysis
    • Kraus D, Panaretos VM. 2012. Dispersion operators and resistant second-order functional data analysis. Biometrika 99:813-32
    • (2012) Biometrika , vol.99 , pp. 813-832
    • Kraus, D.1    Panaretos, V.M.2
  • 126
    • 84875395305 scopus 로고    scopus 로고
    • Fixed and random effects selection in nonparametric additive mixed models
    • Lai RCS, Huang HC, Lee TCM. 2012. Fixed and random effects selection in nonparametric additive mixed models. Electron. J. Stat. 6:810-42
    • (2012) Electron. J. Stat. , vol.6 , pp. 810-842
    • Lai, R.C.S.1    Huang, H.C.2    Lee, T.C.M.3
  • 127
  • 128
    • 33749651702 scopus 로고    scopus 로고
    • Time ordering of gene co-expression
    • Leng X, Müller HG. 2006. Time ordering of gene co-expression. Biostatistics 7:569-84
    • (2006) Biostatistics , vol.7 , pp. 569-584
    • Leng, X.1    Müller, H.G.2
  • 130
    • 84945116550 scopus 로고
    • Sliced inverse regression for dimension reduction
    • Li KC. 1991. Sliced inverse regression for dimension reduction. J. Am. Stat. Assoc. 86:316-27
    • (1991) J. Am. Stat. Assoc. , vol.86 , pp. 316-327
    • Li, K.C.1
  • 131
    • 79952041391 scopus 로고    scopus 로고
    • Identifying cluster number for subspace projected functional data clustering
    • Li PL, Chiou JM. 2011. Identifying cluster number for subspace projected functional data clustering. Comput. Stat. Data Anal. 55:2090-103
    • (2011) Comput. Stat. Data Anal. , vol.55 , pp. 2090-2103
    • Li, P.L.1    Chiou, J.M.2
  • 132
    • 78650108606 scopus 로고    scopus 로고
    • Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data
    • Li Y, Hsing T. 2010. Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data. Ann. Stat. 38:3321-51
    • (2010) Ann. Stat. , vol.38 , pp. 3321-3351
    • Li, Y.1    Hsing, T.2
  • 133
    • 0033475436 scopus 로고    scopus 로고
    • Inference in generalized additive mixed models by using smoothing splines
    • Lin X, Zhang D. 1999. Inference in generalized additive mixed models by using smoothing splines. J. R. Stat. Soc. Ser. B 61:381-400
    • (1999) J. R. Stat. Soc. Ser. B , vol.61 , pp. 381-400
    • Lin, X.1    Zhang, D.2
  • 135
    • 66549101908 scopus 로고    scopus 로고
    • Estimating derivatives for samples of sparsely observed functions, with application to on-line auction dynamics
    • Liu B, Müller HG. 2009. Estimating derivatives for samples of sparsely observed functions, with application to on-line auction dynamics. J. Am. Stat. Assoc. 104:704-14
    • (2009) J. Am. Stat. Assoc. , vol.104 , pp. 704-714
    • Liu, B.1    Müller, H.G.2
  • 136
    • 4944222926 scopus 로고    scopus 로고
    • Functional convex averaging and synchronization for time-warped random curves
    • Liu X, Müller HG. 2004. Functional convex averaging and synchronization for time-warped random curves. J. Am. Stat. Assoc. 99:687-99
    • (2004) J. Am. Stat. Assoc. , vol.99 , pp. 687-699
    • Liu, X.1    Müller, H.G.2
  • 137
    • 25344460476 scopus 로고
    • Fonctions aléatoires à décomposition orthogonale exponentielle
    • Loève M. 1946. Fonctions aléatoires à décomposition orthogonale exponentielle. La Rev. Sci. 84:159-62
    • (1946) La Rev. Sci. , vol.84 , pp. 159-162
    • Loève, M.1
  • 138
    • 84863337880 scopus 로고    scopus 로고
    • A simultaneous confidence band for sparse longitudinal regression
    • Ma S, Yang L, Carroll RJ. 2012. A simultaneous confidence band for sparse longitudinal regression. Stat. Sin. 22:95
    • (2012) Stat. Sin. , vol.22 , pp. 95
    • Ma, S.1    Yang, L.2    Carroll, R.J.3
  • 139
    • 0242595929 scopus 로고    scopus 로고
    • The historical functional linear model
    • Malfait N, Ramsay JO. 2003. The historical functional linear model. Can. J. Stat. 31:115-28
    • (2003) Can. J. Stat. , vol.31 , pp. 115-128
    • Malfait, N.1    Ramsay, J.O.2
  • 140
    • 79960561885 scopus 로고    scopus 로고
    • Multiclass functional discriminant analysis and its application to gesture recognition
    • Matsui H, Araki T, Konishi S. 2011. Multiclass functional discriminant analysis and its application to gesture recognition. J. Classification 28:227-43
    • (2011) J. Classification , vol.28 , pp. 227-243
    • Matsui, H.1    Araki, T.2    Konishi, S.3
  • 144
    • 19744371683 scopus 로고    scopus 로고
    • Functional modelling and classification of longitudinal data
    • Müller HG. 2005. Functional modelling and classification of longitudinal data. Scand. J. Stat. 32:223-40
    • (2005) Scand. J. Stat. , vol.32 , pp. 223-240
    • Müller, H.G.1
  • 145
    • 85057445672 scopus 로고    scopus 로고
    • Functional modeling of longitudinal data
    • ed. G Fitzmaurice, M Davidian, G Verbeke, G Molenberghs Boca Raton, FL: Chapman & Hall
    • Müller HG. 2008. Functional modeling of longitudinal data. In Longitudinal Data Analysis, ed. G Fitzmaurice, M Davidian, G Verbeke, G Molenberghs, pp. 223-52. Boca Raton, FL: Chapman & Hall
    • (2008) Longitudinal Data Analysis , pp. 223-252
    • Müller, H.G.1
  • 146
  • 147
    • 0035819805 scopus 로고    scopus 로고
    • Reproductive potential predicts longevity of female Mediterranean fruit flies
    • MüllerHG, Carey JR, Wu D, Liedo P, Vaupel JW. 2001. Reproductive potential predicts longevity of female Mediterranean fruit flies. Proc. R. Soc. B 268:445-50
    • (2001) Proc. R. Soc. B , vol.268 , pp. 445-450
    • Müller, H.G.1    Carey, J.R.2    Wu, D.3    Liedo, P.4    Vaupel, J.W.5
  • 148
    • 19744372814 scopus 로고    scopus 로고
    • Generalized functional linear models
    • Müller HG, Stadtmüller U. 2005. Generalized functional linear models. Ann. Stat. 33:774-805
    • (2005) Ann. Stat. , vol.33 , pp. 774-805
    • Müller, H.G.1    Stadtmüller, U.2
  • 149
    • 72749115919 scopus 로고    scopus 로고
    • Reproduction is adapted to survival characteristics across geographically isolated medfly populations
    • Müller HG, Wu S, Diamantidis AD, Papadopoulos NT, Carey JR. 2009. Reproduction is adapted to survival characteristics across geographically isolated medfly populations. Proc. R. Soc. B 276:4409-16
    • (2009) Proc. R. Soc. B , vol.276 , pp. 4409-4416
    • Müller, H.G.1    Wu, S.2    Diamantidis, A.D.3    Papadopoulos, N.T.4    Carey, J.R.5
  • 150
    • 84882730974 scopus 로고    scopus 로고
    • Continuously additive models for nonlinear functional regression
    • MüllerHG, WuY, Yao F. 2013. Continuously additive models for nonlinear functional regression. Biometrika 100:607-22
    • (2013) Biometrika , vol.100 , pp. 607-622
    • Müller, H.G.1    Wu, Y.2    Yao, F.3
  • 151
  • 152
    • 78651315755 scopus 로고    scopus 로고
    • Additive modelling of functional gradients
    • Müller HG, Yao F. 2010a. Additive modelling of functional gradients. Biometrika 97:791-805
    • (2010) Biometrika , vol.97 , pp. 791-805
    • Müller, H.G.1    Yao, F.2
  • 153
    • 78650114165 scopus 로고    scopus 로고
    • Empirical dynamics for longitudinal data
    • Müller HG, Yao F. 2010b. Empirical dynamics for longitudinal data. Ann. Stat. 38:3458-86
    • (2010) Ann. Stat. , vol.38 , pp. 3458-3486
    • Müller, H.G.1    Yao, F.2
  • 154
    • 34548786620 scopus 로고    scopus 로고
    • Inferring gene dependency networks from genomic longitudinal data: A functional data approach
    • Opgen-Rhein R, Strimmer K. 2006. Inferring gene dependency networks from genomic longitudinal data: a functional data approach. REVSTAT 4:53-65
    • (2006) REVSTAT , vol.4 , pp. 53-65
    • Opgen-Rhein, R.1    Strimmer, K.2
  • 155
    • 78649438704 scopus 로고    scopus 로고
    • Second-order comparison of Gaussian random functions and the geometry of DNA minicircles
    • Panaretos VM, Kraus D, Maddocks JH. 2010. Second-order comparison of Gaussian random functions and the geometry of DNA minicircles. J. Am. Stat. Assoc. 105:670-82
    • (2010) J. Am. Stat. Assoc. , vol.105 , pp. 670-682
    • Panaretos, V.M.1    Kraus, D.2    Maddocks, J.H.3
  • 156
    • 84879130814 scopus 로고    scopus 로고
    • Fourier analysis of stationary time series in function space
    • Panaretos VM, Tavakoli S. 2013. Fourier analysis of stationary time series in function space. Ann. Stat. 41:568-603
    • (2013) Ann. Stat. , vol.41 , pp. 568-603
    • Panaretos, V.M.1    Tavakoli, S.2
  • 157
    • 68649118122 scopus 로고    scopus 로고
    • Consistency of restricted maximum likelihood estimators of principal components
    • Paul D, Peng J. 2009. Consistency of restricted maximum likelihood estimators of principal components. Ann. Stat. 37:1229-71
    • (2009) Ann. Stat. , vol.37 , pp. 1229-1271
    • Paul, D.1    Peng, J.2
  • 158
    • 77956764784 scopus 로고    scopus 로고
    • Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions
    • Peng J, Müller HG. 2008. Distance-based clustering of sparsely observed stochastic processes, with applications to online auctions. Ann. Appl. Stat. 2:1056-77
    • (2008) Ann. Appl. Stat. , vol.2 , pp. 1056-1077
    • Peng, J.1    Müller, H.G.2
  • 159
    • 84908308461 scopus 로고    scopus 로고
    • Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices
    • Peng J, Paul D, Müller HG. 2014. Time-warped growth processes, with applications to the modeling of boom-bust cycles in house prices. Ann. Appl. Stat. 8:1561-82
    • (2014) Ann. Appl. Stat. , vol.8 , pp. 1561-1582
    • Peng, J.1    Paul, D.2    Müller, H.G.3
  • 161
    • 0001919174 scopus 로고
    • Some properties of smoothed principal components analysis for functional data
    • Pezzulli S, Silverman B. 1993. Some properties of smoothed principal components analysis for functional data. Comput. Stat. 8:1-16
    • (1993) Comput. Stat. , vol.8 , pp. 1-16
    • Pezzulli, S.1    Silverman, B.2
  • 163
    • 0000519382 scopus 로고
    • When the data are functions
    • Ramsay JO. 1982. When the data are functions. Psychometrika 47:379-96
    • (1982) Psychometrika , vol.47 , pp. 379-396
    • Ramsay, J.O.1
  • 164
  • 169
    • 0002289285 scopus 로고
    • Some statistical methods for comparison of growth curves
    • Rao CR. 1958. Some statistical methods for comparison of growth curves. Biometrics 14:1-17
    • (1958) Biometrics , vol.14 , pp. 1-17
    • Rao, C.R.1
  • 170
    • 0001597980 scopus 로고
    • Estimating the mean and covariance structure nonparametrically when the data are curves
    • Rice J, Silverman B. 1991. Estimating the mean and covariance structure nonparametrically when the data are curves. J. R. Stat. Soc. Ser. B 53:233-43
    • (1991) J. R. Stat. Soc. Ser. B , vol.53 , pp. 233-243
    • Rice, J.1    Silverman, B.2
  • 171
    • 8644231058 scopus 로고    scopus 로고
    • Functional and longitudinal data analysis: Perspectives on smoothing
    • Rice JA. 2004. Functional and longitudinal data analysis: perspectives on smoothing. Stat. Sin. 14:631-47
    • (2004) Stat. Sin. , vol.14 , pp. 631-647
    • Rice, J.A.1
  • 172
    • 0035106736 scopus 로고    scopus 로고
    • Nonparametric mixed effects models for unequally sampled noisy curves
    • Rice JA, Wu CO. 2001. Nonparametric mixed effects models for unequally sampled noisy curves. Biometrics 57:253-59
    • (2001) Biometrics , vol.57 , pp. 253-259
    • Rice, J.A.1    Wu, C.O.2
  • 173
    • 84867063756 scopus 로고    scopus 로고
    • Wavelet-RKHS-based functional statistical classification
    • Rincón M, Ruiz-Medina MD. 2012. Wavelet-RKHS-based functional statistical classification. Adv. Data Anal. Classif. 6:201-17
    • (2012) Adv. Data Anal. Classif. , vol.6 , pp. 201-217
    • Rincón, M.1    Ruiz-Medina, M.D.2
  • 174
    • 60449108968 scopus 로고    scopus 로고
    • Bayesian nonparametric functional data analysis through density estimation
    • Rodriguez A, DunsonDB, Gelfand AE. 2009. Bayesian nonparametric functional data analysis through density estimation. Biometrika 96:149-62
    • (2009) Biometrika , vol.96 , pp. 149-162
    • Rodriguez, A.1    Dunson, D.B.2    Gelfand, A.E.3
  • 175
    • 0034704222 scopus 로고    scopus 로고
    • Nonlinear dimensionality reduction by locally linear embedding
    • Roweis ST, Saul LK. 2000. Nonlinear dimensionality reduction by locally linear embedding. Science 290:2323-26
    • (2000) Science , vol.290 , pp. 2323-2326
    • Roweis, S.T.1    Saul, L.K.2
  • 176
    • 0017930815 scopus 로고
    • Dynamic programming algorithm optimization for spoken word recognition
    • Sakoe H, Chiba S. 1978. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26:43-49
    • (1978) IEEE Trans. Acoust. Speech Signal Process. , vol.26 , pp. 43-49
    • Sakoe, H.1    Chiba, S.2
  • 177
    • 24344490799 scopus 로고    scopus 로고
    • Covariate adjusted correlation analysis via varying coefficient models
    • Sentürk D, Müller HG. 2005. Covariate adjusted correlation analysis via varying coefficient models. Scand. J. Stat. 32:365-83
    • (2005) Scand. J. Stat. , vol.32 , pp. 365-383
    • Sentürk, D.1    Müller, H.G.2
  • 178
    • 50949099191 scopus 로고    scopus 로고
    • Generalized varying coefficient models for longitudinal data
    • ŞentürkD, MüllerHG. 2008. Generalized varying coefficient models for longitudinal data. Biometrika 95:653-66
    • (2008) Biometrika , vol.95 , pp. 653-666
    • Şentürk, D.1    Müller, H.G.2
  • 179
    • 24644447890 scopus 로고    scopus 로고
    • CATS: Clustering after transformation and smoothing
    • Serban N, Wasserman L. 2005. CATS: clustering after transformation and smoothing. J. Am. Stat. Assoc. 100:990-99
    • (2005) J. Am. Stat. Assoc. , vol.100 , pp. 990-999
    • Serban, N.1    Wasserman, L.2
  • 180
    • 0041111614 scopus 로고    scopus 로고
    • An analysis of paediatric CD4 counts for acquired immune deficiency syndrome using flexible random curves
    • Shi M, Weiss RE, Taylor JM. 1996. An analysis of paediatric CD4 counts for acquired immune deficiency syndrome using flexible random curves. Appl. Stat. 45:151-63
    • (1996) Appl. Stat. , vol.45 , pp. 151-163
    • Shi, M.1    Weiss, R.E.2    Taylor, J.M.3
  • 181
    • 84911191404 scopus 로고    scopus 로고
    • Canonical correlation analysis for irregularly and sparsely observed functional data
    • Shin H, Lee S 2015. Canonical correlation analysis for irregularly and sparsely observed functional data. J. Multivar. Anal. 134:1-18
    • (2015) J. Multivar. Anal. , vol.134 , pp. 1-18
    • Shin, H.1    Lee, S.2
  • 182
    • 0000858773 scopus 로고
    • Incorporating parametric effects into functional principal components analysis
    • Silverman BW. 1995. Incorporating parametric effects into functional principal components analysis. J. R. Stat. Soc. Ser. B 57:673-89
    • (1995) J. R. Stat. Soc. Ser. B , vol.57 , pp. 673-689
    • Silverman, B.W.1
  • 183
    • 0030537857 scopus 로고    scopus 로고
    • Smoothed functional principal components analysis by choice of norm
    • Silverman BW. 1996. Smoothed functional principal components analysis by choice of norm. Ann. Stat. 24:1-24
    • (1996) Ann. Stat. , vol.24 , pp. 1-24
    • Silverman, B.W.1
  • 184
    • 67449114030 scopus 로고    scopus 로고
    • Functional regression: A new model for predicting market penetration of new products
    • Sood A, James G, Tellis GJ. 2009. Functional regression: a new model for predicting market penetration of new products. Mark. Sci. 28:36-51
    • (2009) Mark. Sci. , vol.28 , pp. 36-51
    • Sood, A.1    James, G.2    Tellis, G.J.3
  • 185
    • 0000713437 scopus 로고
    • Kernel smoothing in partial linear models
    • Speckman P. 1988. Kernel smoothing in partial linear models. J. R. Stat. Soc. Ser. B 50:413-36
    • (1988) J. R. Stat. Soc. Ser. B , vol.50 , pp. 413-436
    • Speckman, P.1
  • 186
    • 0032276659 scopus 로고    scopus 로고
    • Nonparametric regression analysis of longitudinal data
    • Staniswalis JG, Lee JJ. 1998. Nonparametric regression analysis of longitudinal data. J. Am. Stat. Assoc. 93:1403-18
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 1403-1418
    • Staniswalis, J.G.1    Lee, J.J.2
  • 187
    • 0001227575 scopus 로고
    • Additive regression and other nonparametric models
    • Stone CJ. 1985. Additive regression and other nonparametric models. Ann. Stat. 13:689-705
    • (1985) Ann. Stat. , vol.13 , pp. 689-705
    • Stone, C.J.1
  • 189
    • 57249114751 scopus 로고    scopus 로고
    • Pairwise curve synchronization for functional data
    • Tang R, Müller HG. 2008. Pairwise curve synchronization for functional data. Biometrika 95:875-89
    • (2008) Biometrika , vol.95 , pp. 875-889
    • Tang, R.1    Müller, H.G.2
  • 190
    • 0034704229 scopus 로고    scopus 로고
    • A global geometric framework for nonlinear dimensionality reduction
    • Tenenbaum JB, De Silva V, Langford JC. 2000. A global geometric framework for nonlinear dimensionality reduction. Science 290:2319-23
    • (2000) Science , vol.290 , pp. 2319-2323
    • Tenenbaum, J.B.1    De Silva, V.2    Langford, J.C.3
  • 191
    • 84885019648 scopus 로고    scopus 로고
    • Generative models for functional data using phase and amplitude separation
    • Tucker JD, Wu W, Srivastava A. 2013. Generative models for functional data using phase and amplitude separation. Comput. Stat. Data Anal. 61:50-66
    • (2013) Comput. Stat. Data Anal. , vol.61 , pp. 50-66
    • Tucker, J.D.1    Wu, W.2    Srivastava, A.3
  • 192
    • 84865457072 scopus 로고    scopus 로고
    • Inferring stochastic dynamics from functional data
    • Verzelen N, TaoW, MüllerHG. 2012. Inferring stochastic dynamics from functional data. Biometrika 99:533-50
    • (2012) Biometrika , vol.99 , pp. 533-550
    • Verzelen, N.1    Tao, W.2    Müller, H.G.3
  • 194
    • 64549164104 scopus 로고    scopus 로고
    • Polynomial spline confidence bands for regression curves
    • Wang J, Yang L. 2009. Polynomial spline confidence bands for regression curves. Stat. Sin. 19:325-42
    • (2009) Stat. Sin. , vol.19 , pp. 325-342
    • Wang, J.1    Yang, L.2
  • 195
    • 0031486902 scopus 로고    scopus 로고
    • Alignment of curves by dynamic time warping
    • Wang K, Gasser T. 1997. Alignment of curves by dynamic time warping. Ann. Stat. 25:1251-76
    • (1997) Ann. Stat. , vol.25 , pp. 1251-1276
    • Wang, K.1    Gasser, T.2
  • 196
    • 77249135023 scopus 로고    scopus 로고
    • Generalized empirical likelihood methods for analyzing longitudinal data
    • Wang S, Qian L, Carroll RJ. 2010. Generalized empirical likelihood methods for analyzing longitudinal data. Biometrika 97:79-93
    • (2010) Biometrika , vol.97 , pp. 79-93
    • Wang, S.1    Qian, L.2    Carroll, R.J.3
  • 197
    • 35348897501 scopus 로고    scopus 로고
    • Bayesian curve classification using wavelets
    • Wang XH, Ray S, Mallick BK. 2007. Bayesian curve classification using wavelets. J. Am. Stat. Assoc. 102:962-73
    • (2007) J. Am. Stat. Assoc. , vol.102 , pp. 962-973
    • Wang, X.H.1    Ray, S.2    Mallick, B.K.3
  • 198
    • 0034399378 scopus 로고    scopus 로고
    • Kernel smoothing on varying coefficient models with longitudinal dependent variable
    • Wu CO, Chiang CT. 2000. Kernel smoothing on varying coefficient models with longitudinal dependent variable. Stat. Sin. 10:433-56
    • (2000) Stat. Sin. , vol.10 , pp. 433-456
    • Wu, C.O.1    Chiang, C.T.2
  • 199
    • 0032283833 scopus 로고    scopus 로고
    • Asymptotic confidence regions for kernel smoothing of a varyingcoefficient model with longitudinal data
    • Wu CO, Chiang CT, Hoover DR. 1998. Asymptotic confidence regions for kernel smoothing of a varyingcoefficient model with longitudinal data. J. Am. Stat. Assoc. 93:1388-402
    • (1998) J. Am. Stat. Assoc. , vol.93 , pp. 1388-1402
    • Wu, C.O.1    Chiang, C.T.2    Hoover, D.R.3
  • 201
    • 77949502363 scopus 로고    scopus 로고
    • Functional embedding for the classification of gene expression profiles
    • WuP, MüllerHG. 2010. Functional embedding for the classification of gene expression profiles. Bioinformatics 26:509-17
    • (2010) Bioinformatics , vol.26 , pp. 509-517
    • Wu, P.1    Müller, H.G.2
  • 202
    • 84908671871 scopus 로고    scopus 로고
    • Analysis of spike train data: Alignment and comparisons using the extended Fisher-Rao metric
    • Wu W, Srivastava A. 2014. Analysis of spike train data: alignment and comparisons using the extended Fisher-Rao metric. Electron. J. Stat. 8:1776-85
    • (2014) Electron. J. Stat. , vol.8 , pp. 1776-1785
    • Wu, W.1    Srivastava, A.2
  • 203
    • 0036428498 scopus 로고    scopus 로고
    • An adaptive estimation of dimension reduction space
    • Xia Y, Tong H, Li W, Zhu LX. 2002. An adaptive estimation of dimension reduction space. J. R. Stat. Soc. Ser. B 64:363-410
    • (2002) J. R. Stat. Soc. Ser. B , vol.64 , pp. 363-410
    • Xia, Y.1    Tong, H.2    Li, W.3    Zhu, L.X.4
  • 205
    • 33645031408 scopus 로고    scopus 로고
    • Penalized spline models for functional principal component analysis
    • Yao F, Lee T. 2006. Penalized spline models for functional principal component analysis. J. R. Stat. Soc. Ser. B 68:3-25
    • (2006) J. R. Stat. Soc. Ser. B , vol.68 , pp. 3-25
    • Yao, F.1    Lee, T.2
  • 206
    • 77249159873 scopus 로고    scopus 로고
    • Functional quadratic regression
    • Yao F, Müller HG. 2010. Functional quadratic regression. Biometrika 97:49-64
    • (2010) Biometrika , vol.97 , pp. 49-64
    • Yao, F.1    Müller, H.G.2
  • 207
    • 19744375466 scopus 로고    scopus 로고
    • Functional data analysis for sparse longitudinal data
    • Yao F, Müller HG, Wang JL. 2005a. Functional data analysis for sparse longitudinal data. J. Am. Stat. Assoc. 100:577-90
    • (2005) J. Am. Stat. Assoc. , vol.100 , pp. 577-590
    • Yao, F.1    Müller, H.G.2    Wang, J.L.3
  • 208
    • 19744369661 scopus 로고    scopus 로고
    • Functional linear regression analysis for longitudinal data
    • Yao F, Müller HG, Wang JL. 2005b. Functional linear regression analysis for longitudinal data. Ann. Stat. 33:2873-903
    • (2005) Ann. Stat. , vol.33 , pp. 2873-2903
    • Yao, F.1    Müller, H.G.2    Wang, J.L.3
  • 209
    • 35348861403 scopus 로고    scopus 로고
    • Two-stage efficient estimation of longitudinal nonparametric additive models
    • You J, Zhou H. 2007. Two-stage efficient estimation of longitudinal nonparametric additive models. Stat. Probability Lett. 77:1666-75
    • (2007) Stat. Probability Lett. , vol.77 , pp. 1666-1675
    • You, J.1    Zhou, H.2
  • 211
    • 84890102885 scopus 로고    scopus 로고
    • Time-varying additive models for longitudinal data
    • Zhang X, Park BU, Wang JL. 2013. Time-varying additive models for longitudinal data. J. Am. Stat. Assoc. 108:983-98
    • (2013) J. Am. Stat. Assoc. , vol.108 , pp. 983-998
    • Zhang, X.1    Park, B.U.2    Wang, J.L.3
  • 212
    • 84941568487 scopus 로고    scopus 로고
    • Varying-coefficient additive models for functional data
    • Zhang X, Wang JL. 2015. Varying-coefficient additive models for functional data. Biometrika 102:15-32
    • (2015) Biometrika , vol.102 , pp. 15-32
    • Zhang, X.1    Wang, J.L.2
  • 215
    • 8644276476 scopus 로고    scopus 로고
    • The functional data analysis view of longitudinal data
    • Zhao X, Marron JS, Wells MT. 2004. The functional data analysis view of longitudinal data. Stat. Sin. 14:789-808
    • (2004) Stat. Sin. , vol.14 , pp. 789-808
    • Zhao, X.1    Marron, J.S.2    Wells, M.T.3
  • 216
    • 84907527507 scopus 로고    scopus 로고
    • Spatially varying coefficient model for neuroimaging data with jump discontinuities
    • Zhu H, Fan J, Kong L. 2014. Spatially varying coefficient model for neuroimaging data with jump discontinuities. J. Am. Stat. Assoc. 109:1084-98
    • (2014) J. Am. Stat. Assoc. , vol.109 , pp. 1084-1098
    • Zhu, H.1    Fan, J.2    Kong, L.3
  • 217
    • 84871642334 scopus 로고    scopus 로고
    • Robust classification of functional and quantitative image data using functional mixed models
    • Zhu HX, Brown PJ, Morris JS. 2012. Robust classification of functional and quantitative image data using functional mixed models. Biometrics 68:1260-68
    • (2012) Biometrics , vol.68 , pp. 1260-1268
    • Zhu, H.X.1    Brown, P.J.2    Morris, J.S.3
  • 218
    • 77952974130 scopus 로고    scopus 로고
    • A Bayesian hierarchical model for classification with selection of functional predictors
    • Zhu HX, Vannucci M, Cox DD. 2010. A Bayesian hierarchical model for classification with selection of functional predictors. Biometrics 66:463-73
    • (2010) Biometrics , vol.66 , pp. 463-473
    • Zhu, H.X.1    Vannucci, M.2    Cox, D.D.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.