메뉴 건너뛰기




Volumn 17, Issue 1-2, 2017, Pages 1-35

A general framework for functional regression modelling

Author keywords

functional additive mixed model; Functional data; functional principal components; GAMLSS; gradient boosting; penalized splines

Indexed keywords


EID: 85014673804     PISSN: 1471082X     EISSN: 14770342     Source Type: Journal    
DOI: 10.1177/1471082X16681317     Document Type: Article
Times cited : (106)

References (115)
  • 1
    • 85014597986 scopus 로고    scopus 로고
    • Functional regression models for location, scale and shape with application to stock returns
    • Friedl, Wagner, (eds), a),. In, eds.,., Linz, July 6–10, 2015, pages
    • Brockhaus S, Fuest A, Mayr A, Greven S, (2015a) Functional regression models for location, scale and shape with application to stock returns. In Friedl H, Wagner H, eds. Proceedings of the 30th International Workshop on Statistical Modelling, vol. 1, Linz, July 6–10, 2015, pages 117–22.
    • (2015) Proceedings of the 30th International Workshop on Statistical Modelling , vol.1 , pp. 117-122
    • Brockhaus, S.1    Fuest, A.2    Mayr, A.3    Greven, S.4
  • 3
    • 84969793663 scopus 로고    scopus 로고
    • Boosting flexible functional regre- ssion models with a high number of functional historical effects
    • b),.,. (accessed on 17 November 2016)
    • Brockhaus S, Melcher M, Leisch F, Greven S, (2016b) Boosting flexible functional regre- ssion models with a high number of functional historical effects. Statistics and Computing. Available at http://link.springer.com/article/10.1007/s11222-016-9662-1. (accessed on 17 November 2016).
    • (2016) Statistics and Computing
    • Brockhaus, S.1    Melcher, M.2    Leisch, F.3    Greven, S.4
  • 6
    • 41549141939 scopus 로고    scopus 로고
    • Boosting algo- rithms: Regularization, prediction and model fitting
    • Bühlmann P, Hothorn T, (2007) Boosting algo- rithms: Regularization, prediction and model fitting. Statistical Science, 22, 477–505.
    • (2007) Statistical Science , vol.22 , pp. 477-505
    • Bühlmann, P.1    Hothorn, T.2
  • 8
    • 84957882244 scopus 로고    scopus 로고
    • Functional linear mixed models for irregularly or sparsely sampled data
    • Cederbaum J, Pouplier M, Hoole P, Greven S, (2016) Functional linear mixed models for irregularly or sparsely sampled data. Statistical Modelling, 16, 67–88.
    • (2016) Statistical Modelling , vol.16 , pp. 67-88
    • Cederbaum, J.1    Pouplier, M.2    Hoole, P.3    Greven, S.4
  • 12
    • 84908687122 scopus 로고    scopus 로고
    • Shape and object data analysis
    • Dryden IL, (2014) Shape and object data analysis. Biometrical Journal, 56, 758–60.
    • (2014) Biometrical Journal , vol.56 , pp. 758-760
    • Dryden, I.L.1
  • 13
    • 25444532788 scopus 로고    scopus 로고
    • Flexible smoothing with B-splines and penalties
    • Eilers P, Marx B, (1996) Flexible smoothing with B-splines and penalties. Statistical Sciences, 11, 89–121.
    • (1996) Statistical Sciences , vol.11 , pp. 89-121
    • Eilers, P.1    Marx, B.2
  • 14
    • 0037613485 scopus 로고    scopus 로고
    • Multivariate calibration with temperature interaction using two-dimensional penalized signal regression
    • Eilers PH, Marx BD, (2003) Multivariate calibration with temperature interaction using two-dimensional penalized signal regression. Chemometrics and intelligent laboratory systems, 66, 159–74.
    • (2003) Chemometrics and intelligent laboratory systems , vol.66 , pp. 159-174
    • Eilers, P.H.1    Marx, B.D.2
  • 16
    • 84956704476 scopus 로고    scopus 로고
    • Spatial regression models over two-dimensional manifolds
    • Ettinger B, Perotto S, Sangalli LM, (2016) Spatial regression models over two-dimensional manifolds. Biometrika, 103, 71–88.
    • (2016) Biometrika , vol.103 , pp. 71-88
    • Ettinger, B.1    Perotto, S.2    Sangalli, L.M.3
  • 17
    • 84868156165 scopus 로고    scopus 로고
    • Statistical computing in functional data analysis: The R package
    • , (accessed on 17 November 2016)
    • Febrero-Bande M, Oviedo de la Fuente M, (2012) Statistical computing in functional data analysis: The R package fda.usc. Journal of Statistical Software, 51, 1–28. Available at http://www.jstatsoft.org/v51/i04/ (accessed on 17 November 2016).
    • (2012) Journal of Statistical Software , vol.51 , pp. 1-28
    • Febrero-Bande, M.1    Oviedo de la Fuente, M.2
  • 18
    • 79960127235 scopus 로고    scopus 로고
    • Identifying risk factors for severe childhood malnutrition by boosting additive quantile regression
    • Fenske N, Kneib T, Hothorn T, (2011) Identifying risk factors for severe childhood malnutrition by boosting additive quantile regression. Journal of the American Statistical Association, 106, 494–510.
    • (2011) Journal of the American Statistical Association , vol.106 , pp. 494-510
    • Fenske, N.1    Kneib, T.2    Hothorn, T.3
  • 22
    • 84879076970 scopus 로고    scopus 로고
    • Longitudinal scalar-on- functions regression with application to tractography data
    • a)
    • Gertheiss J, Goldsmith J, Crainiceanu C, Greven S, (2013a) Longitudinal scalar-on- functions regression with application to tractography data. Biostatistics, 14, 447–61.
    • (2013) Biostatistics , vol.14 , pp. 447-461
    • Gertheiss, J.1    Goldsmith, J.2    Crainiceanu, C.3    Greven, S.4
  • 23
    • 84900855077 scopus 로고    scopus 로고
    • Variable selection in generalized functional linear models
    • b)
    • Gertheiss J, Maity A, Staicu A-M, (2013b) Variable selection in generalized functional linear models. Stat, 2, 86–101.
    • (2013) Stat , vol.2 , pp. 86-101
    • Gertheiss, J.1    Maity, A.2    Staicu, A.-M.3
  • 24
    • 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
  • 26
    • 84875947724 scopus 로고    scopus 로고
    • Corrected confidence bands for functional data using principal components
    • Goldsmith J, Greven S, Crainiceanu CM, (2013) Corrected confidence bands for functional data using principal components. Biometrics, 69, 41–51.
    • (2013) Biometrics , vol.69 , pp. 41-51
    • Goldsmith, J.1    Greven, S.2    Crainiceanu, C.M.3
  • 29
    • 84931577988 scopus 로고    scopus 로고
    • Generalized multilevel function-on-scalar regression and principal component analysis
    • Goldsmith J, Zipunnikov V, Schrack J, (2015) Generalized multilevel function-on-scalar regression and principal component analysis. Biometrics, 71, 344–53.
    • (2015) Biometrics , vol.71 , pp. 344-353
    • Goldsmith, J.1    Zipunnikov, V.2    Schrack, J.3
  • 32
    • 84936773585 scopus 로고    scopus 로고
    • Unifying amplitude and phase analysis: A compositional data approach to functional multivariate mixed- effects modeling of mandarin Chinese
    • Hadjipantelis PZ, Aston JA, Müller H-G, Evans JP, (2015) Unifying amplitude and phase analysis: A compositional data approach to functional multivariate mixed- effects modeling of mandarin Chinese. Journal of the American Statistical Association, 110, 545–59.
    • (2015) Journal of the American Statistical Association , vol.110 , pp. 545-559
    • Hadjipantelis, P.Z.1    Aston, J.A.2    Müller, H.-G.3    Evans, J.P.4
  • 34
    • 0000467952 scopus 로고
    • Discussion of ‘A statistical view of some chemometrics reg- ression tools,’ by I. E. Frank and J. H. Friedman
    • Hastie T, Mallows R, (1993) Discussion of ‘A statistical view of some chemometrics reg- ression tools,’ by I. E. Frank and J. H. Friedman. Technometrics, 35, 140–43.
    • (1993) Technometrics , vol.35 , pp. 140-143
    • Hastie, T.1    Mallows, R.2
  • 37
    • 85014606964 scopus 로고    scopus 로고
    • The University of Texas M.D. Anderson Cancer Center, version 3.0 edition, (accessed on 16 December 2016)
    • Herrick R, (2015) WFMM. The University of Texas M.D. Anderson Cancer Center, version 3.0 edition. Available at https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware.aspx?Software_Id=70 (accessed on 16 December 2016)
    • (2015) WFMM
    • Herrick, R.1
  • 45
    • 0001699048 scopus 로고    scopus 로고
    • Principal component models for sparse functional data
    • James GM, Hastie TJ, Sugar CA, (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.A.3
  • 48
    • 66949120727 scopus 로고    scopus 로고
    • Variable selection and model choice in geoadditive regression models
    • Kneib T, Hothorn T, Tutz G, (2009) Variable selection and model choice in geoadditive regression models. Biometrics, 65, 626–34.
    • (2009) Biometrics , vol.65 , pp. 626-634
    • Kneib, T.1    Hothorn, T.2    Tutz, G.3
  • 49
    • 84925105967 scopus 로고    scopus 로고
    • Cambridge, Cambridge University Press
    • Koenker R, (2005) Quantile Regression. Cambridge: Cambridge University Press.
    • (2005) Quantile Regression
    • Koenker, R.1
  • 53
    • 84857058351 scopus 로고    scopus 로고
    • Coverage proper- ties of confidence intervals for generalized additive model components
    • Marra G, Wood SN, (2012) Coverage proper- ties of confidence intervals for generalized additive model components. Scandinavian Journal of Statistics, 39, 53–74.
    • (2012) Scandinavian Journal of Statistics , vol.39 , pp. 53-74
    • Marra, G.1    Wood, S.N.2
  • 54
    • 84906729542 scopus 로고    scopus 로고
    • Overview of object oriented data analysis
    • Marron JS, Alonso AM, (2014) Overview of object oriented data analysis. Biometrical Journal, 56, 732–53.
    • (2014) Biometrical Journal , vol.56 , pp. 732-753
    • Marron, J.S.1    Alonso, A.M.2
  • 56
    • 0033079479 scopus 로고    scopus 로고
    • Generalized linear regression on sampled signals and curves: A P-spline approach
    • Marx BD, Eilers PH, (1999) Generalized linear regression on sampled signals and curves: A P-spline approach. Technometrics, 41, 1–13.
    • (1999) Technometrics , vol.41 , pp. 1-13
    • Marx, B.D.1    Eilers, P.H.2
  • 57
    • 13444257535 scopus 로고    scopus 로고
    • Multidimensional penalized signal regression
    • Marx BD, Eilers PH, (2005) Multidimensional penalized signal regression. Technometrics, 47, 13–22.
    • (2005) Technometrics , vol.47 , pp. 13-22
    • Marx, B.D.1    Eilers, P.H.2
  • 59
    • 84938421299 scopus 로고    scopus 로고
    • Restricted likelihood ratio tests for linearity in scalar-on-function regression
    • McLean MW, Hooker G, Ruppert D, (2015) Restricted likelihood ratio tests for linearity in scalar-on-function regression. Statistics and Computing, 25, 997–1008.
    • (2015) Statistics and Computing , vol.25 , pp. 997-1008
    • McLean, M.W.1    Hooker, G.2    Ruppert, D.3
  • 62
    • 0001500115 scopus 로고
    • Functions of positive and negative type, and their connection with the theory of integral equations
    • Mercer J, (1909) Functions of positive and negative type, and their connection with the theory of integral equations. Philosophical Transactions of the Royal Society of London. Series A, 209, 415–46.
    • (1909) Philosophical Transactions of the Royal Society of London. Series A , vol.209 , pp. 415-446
    • Mercer, J.1
  • 63
    • 84941743821 scopus 로고    scopus 로고
    • Bayesian function-on- function regression for multilevel functional data
    • Meyer MJ, Coull BA, Versace F, Cinciripini P, Morris JS, (2015) Bayesian function-on- function regression for multilevel functional data. Biometrics, 71, 563–74.
    • (2015) Biometrics , vol.71 , pp. 563-574
    • Meyer, M.J.1    Coull, B.A.2    Versace, F.3    Cinciripini, P.4    Morris, J.S.5
  • 66
    • 80054689503 scopus 로고    scopus 로고
    • Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data
    • Morris JS, Baladandayuthapani V, Herrick RC, Sanna P, Gutstein H, (2011) Automated analysis of quantitative image data using isomorphic functional mixed models, with application to proteomics data. The Annals of Applied Statistics, 5, 894–923.
    • (2011) The Annals of Applied Statistics , vol.5 , pp. 894-923
    • Morris, J.S.1    Baladandayuthapani, V.2    Herrick, R.C.3    Sanna, P.4    Gutstein, H.5
  • 70
    • 84972545853 scopus 로고
    • A statistical perspective on ill-posed inverse problems
    • O'Sullivan F, (1986) A statistical perspective on ill-posed inverse problems. Statistical Science, 1, 502–18.
    • (1986) Statistical Science , vol.1 , pp. 502-518
    • O'Sullivan, F.1
  • 71
    • 72449195207 scopus 로고    scopus 로고
    • A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data
    • Peng J, Paul D, (2012) A geometric approach to maximum likelihood estimation of the functional principal components from sparse longitudinal data. Journal of Compu- tational and Graphical Statistics, 18, 995–1015.
    • (2012) Journal of Compu- tational and Graphical Statistics , vol.18 , pp. 995-1015
    • Peng, J.1    Paul, D.2
  • 72
    • 84971620376 scopus 로고    scopus 로고
    • R package version 4-6. (accessed on 23 November 2016)
    • Plummer M, (2016) rjags: Bayesian Graphical Models Using MCMC. R package version 4-6. Available at https://CRAN.R-project.org/package=rjags. (accessed on 23 November 2016).
    • (2016) rjags: Bayesian Graphical Models Using MCMC
    • Plummer, M.1
  • 77
    • 84969844589 scopus 로고    scopus 로고
    • Estimating variance components in functional linear models with applications to genetic heritability
    • Reimherr M, Nicolae D, (2016) Estimating variance components in functional linear models with applications to genetic heritability. Journal of the American Statis- tical Association, 111, 407–22.
    • (2016) Journal of the American Statis- tical Association , vol.111 , pp. 407-422
    • Reimherr, M.1    Nicolae, D.2
  • 78
    • 35348906983 scopus 로고    scopus 로고
    • Functional principal component regression and functional partial least squares
    • Reiss PT, Ogden RT, (2007) Functional principal component regression and functional partial least squares. Journal of the American Statistical Association, 102, 984–96.
    • (2007) Journal of the American Statistical Association , vol.102 , pp. 984-996
    • Reiss, P.T.1    Ogden, R.T.2
  • 82
    • 35348906983 scopus 로고    scopus 로고
    • Functional prin- cipal component regression and functional partial least squares
    • Reiss PT, Ogden RT, (2007) Functional prin- cipal component regression and functional partial least squares. Journal of the American Statistical Association, 102, 984–96.
    • (2007) Journal of the American Statistical Association , vol.102 , pp. 984-996
    • Reiss, P.T.1    Ogden, R.T.2
  • 86
    • 84964047394 scopus 로고    scopus 로고
    • Identifiability in penalized function-on-function regression models
    • Scheipl F, Greven S, (2016) Identifiability in penalized function-on-function regression models. Electronic Journal of Statistics, 10, 495–526.
    • (2016) Electronic Journal of Statistics , vol.10 , pp. 495-526
    • Scheipl, F.1    Greven, S.2
  • 87
    • 40249103367 scopus 로고    scopus 로고
    • Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models
    • Scheipl F, Greven S, Küchenhoff H, (2008) Size and power of tests for a zero random effect variance or polynomial regression in additive and linear mixed models. Computational Statistics & Data Analysis, 52, 3283–99.
    • (2008) Computational Statistics & Data Analysis , vol.52 , pp. 3283-3299
    • Scheipl, F.1    Greven, S.2    Küchenhoff, H.3
  • 93
    • 34548459917 scopus 로고    scopus 로고
    • Gaussian process functional regression modeling for batch data
    • Shi JQ, Wang B, Murray-Smith R, Titterington M, (2007) Gaussian process functional regression modeling for batch data. Biometrics, 63, 714–23.
    • (2007) Biometrics , vol.63 , pp. 714-723
    • Shi, J.Q.1    Wang, B.2    Murray-Smith, R.3    Titterington, M.4
  • 95
    • 84920465588 scopus 로고    scopus 로고
    • Likelihood ratio tests for dependent data with applications to longitudinal and functional data analysis
    • Staicu A-M, Li Y, Crainiceanu CM, Ruppert D, (2014) Likelihood ratio tests for dependent data with applications to longitudinal and functional data analysis. Scandinavian Journal of Statistics, 41, 932–49.
    • (2014) Scandinavian Journal of Statistics , vol.41 , pp. 932-949
    • Staicu, A.-M.1    Li, Y.2    Crainiceanu, C.M.3    Ruppert, D.4
  • 97
    • 84918504141 scopus 로고    scopus 로고
    • Restricted likelihood ratio tests for functional effects in the functional linear model
    • Swihart BJ, Goldsmith J, Crainiceanu CM, (2014) Restricted likelihood ratio tests for functional effects in the functional linear model. Technometrics, 56, 483–93.
    • (2014) Technometrics , vol.56 , pp. 483-493
    • Swihart, B.J.1    Goldsmith, J.2    Crainiceanu, C.M.3
  • 99
    • 77949774437 scopus 로고    scopus 로고
    • Bayesian functional principal components analysis for binary and count data
    • Van der Linde A, (2009) Bayesian functional principal components analysis for binary and count data. Advances in Statistical Analysis, 93, 307–33.
    • (2009) Advances in Statistical Analysis , vol.93 , pp. 307-333
    • Van der Linde, A.1
  • 101
    • 84907522902 scopus 로고    scopus 로고
    • Generalized Gaussian process regression model for non-Gaussian functional data
    • Wang B, Shi JQ, (2014) Generalized Gaussian process regression model for non-Gaussian functional data. Journal of the American Statistical Association, 109, 1123–33.
    • (2014) Journal of the American Statistical Association , vol.109 , pp. 1123-1133
    • Wang, B.1    Shi, J.Q.2
  • 102
    • 50449109596 scopus 로고    scopus 로고
    • Object-oriented data analysis: Sets of trees
    • Wang H, Marron J, (2007) Object-oriented data analysis: Sets of trees. The Annals of Statistics, 35, 1849–73.
    • (2007) The Annals of Statistics , vol.35 , pp. 1849-1873
    • Wang, H.1    Marron, J.2
  • 105
    • 33645690420 scopus 로고    scopus 로고
    • Low-rank scale-invariant tensor product smooths for generalized additive mixed models
    • b)
    • Wood SN, (2006b) Low-rank scale-invariant tensor product smooths for generalized additive mixed models. Biometrics, 62, 1025–36.
    • (2006) Biometrics , vol.62 , pp. 1025-1036
    • Wood, S.N.1
  • 106
    • 78650862532 scopus 로고    scopus 로고
    • Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models
    • Wood SN, (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. Journal of the Royal Statistical Society: Series B, 73, 3–36.
    • (2011) Journal of the Royal Statistical Society: Series B , vol.73 , pp. 3-36
    • Wood, S.N.1
  • 107
    • 84890397927 scopus 로고    scopus 로고
    • A simple test for random effects in regression models
    • Wood SN, (2013) A simple test for random effects in regression models. Biometrika, 100, 1005–10.
    • (2013) Biometrika , vol.100 , pp. 1005-1010
    • Wood, S.N.1
  • 108
    • 85055753151 scopus 로고    scopus 로고
    • Just another Gibbs additive modeller: Interfacing JAGS and mgcv
    • a),., (accessed on 23 November 2016)
    • Wood SN, (2016a) Just another Gibbs additive modeller: Interfacing JAGS and mgcv. arXiv preprint arXiv:1602.02539. Available at https://arxiv.org/abs/1602.02539 (accessed on 23 November 2016).
    • (2016) arXiv preprint arXiv:1602.02539
    • Wood, S.N.1
  • 110
    • 85018208819 scopus 로고    scopus 로고
    • Generalized additive models for gigadata: Modelling the UK black smoke network daily data
    • Wood SN, Li Z, Shaddick G, Augustin NH, (2016a) Generalized additive models for gigadata: Modelling the UK black smoke network daily data. Journal of the Ameri- can Statistical Association. Available at http://amstat.tandfonline.com/doi/abs/10.1080/01621459.2016.1195744
    • (2016) Journal of the Ameri- can Statistical Association
    • Wood, S.N.1    Li, Z.2    Shaddick, G.3    Augustin, N.H.4
  • 111
    • 85010676878 scopus 로고    scopus 로고
    • Smoothing parameter and model selection for general smooth models
    • b),., (accessed on 23 November 2016)
    • Wood SN, Pya N, Säfken B, (2016b) Smoothing parameter and model selection for general smooth models. Journal of the American Statistical Association. Available at http://arxiv.org/abs/1511.03864 (accessed on 23 November 2016).
    • (2016) Journal of the American Statistical Association
    • Wood, S.N.1    Pya, N.2    Säfken, B.3
  • 113
    • 19744369661 scopus 로고    scopus 로고
    • Functional linear regression analysis for longitudinal data
    • b)
    • Yao F, Müller H, Wang J, (2005b) Functional linear regression analysis for longitudinal data. The Annals of Statistics, 33, 2873–903.
    • (2005) The Annals of Statistics , vol.33 , pp. 2873-2903
    • Yao, F.1    Müller, H.2    Wang, J.3
  • 115
    • 84919439034 scopus 로고    scopus 로고
    • Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis
    • Zipunnikov V, Greven S, Shou H, Caffo B, Reich DS, Crainiceanu C, (2014) Longitudinal high-dimensional principal components analysis with application to diffusion tensor imaging of multiple sclerosis. The Annals of Applied Statistics, 8, 2175–202.
    • (2014) The Annals of Applied Statistics , vol.8 , pp. 2175-2202
    • Zipunnikov, V.1    Greven, S.2    Shou, H.3    Caffo, B.4    Reich, D.S.5    Crainiceanu, C.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.