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Volumn 28, Issue 2, 2016, Pages 338-359

Norm-preserving constraint in the Fisher–Rao registration and its application in signal estimation

Author keywords

alignment with constraint; functional data analysis; registration; signal estimation; time warping

Indexed keywords


EID: 84961393136     PISSN: 10485252     EISSN: 10290311     Source Type: Journal    
DOI: 10.1080/10485252.2016.1163353     Document Type: Article
Times cited : (2)

References (35)
  • 1
    • 33846409110 scopus 로고    scopus 로고
    • Semi-Functional Partial Linear Regression
    • G.Aneiros-Perez,, and P.Vieu, (2006), ‘Semi-Functional Partial Linear Regression’, Statistics and Probability Letters, 76, 1102–1110. doi: 10.1016/j.spl.2005.12.007
    • (2006) Statistics and Probability Letters , vol.76 , pp. 1102-1110
    • Aneiros-Perez, G.1    Vieu, P.2
  • 2
    • 84863566083 scopus 로고    scopus 로고
    • Robust Depth-Based Estimation in The Time Warping Model
    • A.Arribas-Gil,, and J.Romo, (2012), ‘Robust Depth-Based Estimation in The Time Warping Model’, Biostatistics, 13, 398–414. doi: 10.1093/biostatistics/kxr037
    • (2012) Biostatistics , vol.13 , pp. 398-414
    • Arribas-Gil, A.1    Romo, J.2
  • 3
    • 84964260565 scopus 로고    scopus 로고
    • Norm-preserving Discretization of Integral Equations for Elliptic PDEs with Internal Layers I: The One-dimensional Case. arXiv:1305.6976v1
    • T.Askham,, and L.Greengard, (2013), Norm-preserving Discretization of Integral Equations for Elliptic PDEs with Internal Layers I: The One-dimensional Case. arXiv:1305.6976v1.
    • (2013)
    • Askham, T.1    Greengard, L.2
  • 6
    • 84860885088 scopus 로고    scopus 로고
    • Single and Multiple Index Functional Regression Models With Nonparametric Link
    • D.Chen,, P.Hall,, and H.-G.Müller, (2011), ‘Single and Multiple Index Functional Regression Models With Nonparametric Link’, The Annals of Statistics, 39, 1720–1747. doi: 10.1214/11-AOS882
    • (2011) The Annals of Statistics , vol.39 , pp. 1720-1747
    • Chen, D.1    Hall, P.2    Müller, H.-G.3
  • 7
    • 84892512066 scopus 로고    scopus 로고
    • A Partial Overview of The Theory of Statistics With Functional Data
    • A.Cuevas, (2014), ‘A Partial Overview of The Theory of Statistics With Functional Data’, Journal of Statistical Planning and Inference, 147, 1–23. doi: 10.1016/j.jspi.2013.04.002
    • (2014) Journal of Statistical Planning and Inference , vol.147 , pp. 1-23
    • Cuevas, A.1
  • 8
    • 84888287831 scopus 로고    scopus 로고
    • A Robust Algorithm For Template Curve Estimation Based on Manifold Embedding
    • C.Dimeglio,, S.Gallon,, J.M.Loubes,, and E.Maza, (2014), ‘A Robust Algorithm For Template Curve Estimation Based on Manifold Embedding’, Computational Statistics and Data Analysis, 70, 373–386. doi: 10.1016/j.csda.2013.09.030
    • (2014) Computational Statistics and Data Analysis , vol.70 , pp. 373-386
    • Dimeglio, C.1    Gallon, S.2    Loubes, J.M.3    Maza, E.4
  • 9
    • 78650306671 scopus 로고    scopus 로고
    • Non Parametric Estimation of The Structural Expectation of a Stochastic Increasing Function
    • J.F.Dupuy,, J.M.Loubes,, and E.Maza, (2011), ‘Non Parametric Estimation of The Structural Expectation of a Stochastic Increasing Function’, Statistics and Computing, 21, 121–136. doi: 10.1007/s11222-009-9152-9
    • (2011) Statistics and Computing , vol.21 , pp. 121-136
    • Dupuy, J.F.1    Loubes, J.M.2    Maza, E.3
  • 10
    • 84877924893 scopus 로고    scopus 로고
    • Functional Projection Pursuit Regression
    • F.Ferraty,, A.Goia,, E.Sallinelli,, and P.Vieu, (2013), ‘Functional Projection Pursuit Regression’, TEST, 22, 293–320. doi: 10.1007/s11749-012-0306-2
    • (2013) TEST , vol.22 , pp. 293-320
    • Ferraty, F.1    Goia, A.2    Sallinelli, E.3    Vieu, P.4
  • 14
    • 27944498298 scopus 로고    scopus 로고
    • Nonparametric Maximum Likelihood Estimation of the Structural Mean of a Sample of Curves
    • D.Gervini,, and T.Gasser, (2005), ‘Nonparametric Maximum Likelihood Estimation of the Structural Mean of a Sample of Curves’, Biometrika, 92, 801–820. doi: 10.1093/biomet/92.4.801
    • (2005) Biometrika , vol.92 , pp. 801-820
    • Gervini, D.1    Gasser, T.2
  • 15
    • 84942303196 scopus 로고    scopus 로고
    • A partitioned Single Functional Index Model
    • A.Goia,, and P.Vieu, (2014), ‘A partitioned Single Functional Index Model’, Computational Statistics, 30, 673–692. doi: 10.1007/s00180-014-0530-1
    • (2014) Computational Statistics , vol.30 , pp. 673-692
    • Goia, A.1    Vieu, P.2
  • 16
    • 84964236775 scopus 로고    scopus 로고
    • An Exploration of Random Processes for Engineers, e-book, ark:/13960/t1hh7t89d
    • B.Hajek, (2011), An Exploration of Random Processes for Engineers, e-book, ark:/13960/t1hh7t89d.
    • (2011)
    • Hajek, B.1
  • 17
    • 38049110678 scopus 로고    scopus 로고
    • A Method for Projecting Functional Data onto a Low-Dimensional Space
    • P.Hall,, Y.K.Lee, and B.U.Park (2007), ‘A Method for Projecting Functional Data onto a Low-Dimensional Space’, Journal of Computational and Graphical Statistics, 16, 799–812. doi: 10.1198/106186007X257296
    • (2007) Journal of Computational and Graphical Statistics , vol.16 , pp. 799-812
    • Hall, P.1    Lee, Y.K.2    Park, B.U.3
  • 19
    • 49949089272 scopus 로고    scopus 로고
    • Curve Alignments by Moments
    • G.M.James, (2007), ‘Curve Alignments by Moments’, Annals of Applied Statistics, 1, 480–501. doi: 10.1214/07-AOAS127
    • (2007) Annals of Applied Statistics , vol.1 , pp. 480-501
    • James, G.M.1
  • 20
    • 21144472058 scopus 로고
    • Statistical Tools to Analyze Data Representing a Sample of Curves
    • A.Kneip,, and T.Gasser, (1992), ‘Statistical Tools to Analyze Data Representing a Sample of Curves’, The Annals of Statistics, 20, 1266–1305. doi: 10.1214/aos/1176348769
    • (1992) The Annals of Statistics , vol.20 , pp. 1266-1305
    • Kneip, A.1    Gasser, T.2
  • 21
    • 54949101671 scopus 로고    scopus 로고
    • Combining Registration and Fitting for Functional Models
    • A.Kneip,, and J.O.Ramsay, (2008), ‘Combining Registration and Fitting for Functional Models’, Journal of American Statistical Association, 103, 1155–1165. doi: 10.1198/016214508000000517
    • (2008) Journal of American Statistical Association , vol.103 , pp. 1155-1165
    • Kneip, A.1    Ramsay, J.O.2
  • 22
    • 84878123900 scopus 로고    scopus 로고
    • Uniform Consistency of kNN Regressors for Functional Variables
    • N.I.Kudraszow, and P.Vieu, (2013), ‘Uniform Consistency of kNN Regressors for Functional Variables’, Statistics and Probability Letters, 83, 1863–1870. doi: 10.1016/j.spl.2013.04.017
    • (2013) Statistics and Probability Letters , vol.83 , pp. 1863-1870
    • Kudraszow, N.I.1    Vieu, P.2
  • 23
    • 85162460549 scopus 로고    scopus 로고
    • ‘Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment,’ in Proceedings of Neural Information Processing Systems (NIPS)
    • S.Kurtek,, A.Srivastava,, and W.Wu, (December 2011), ‘Signal Estimation Under Random Time-Warpings and Nonlinear Signal Alignment,’ in Proceedings of Neural Information Processing Systems (NIPS).
    • (2011)
    • Kurtek, S.1    Srivastava, A.2    Wu, W.3
  • 24
    • 79952474151 scopus 로고    scopus 로고
    • Functional Partial Linear Model
    • H.Lian, (2011), ‘Functional Partial Linear Model’, Journal of Nonparametric Statistics, 23, 115–128. doi: 10.1080/10485252.2010.500385
    • (2011) Journal of Nonparametric Statistics , vol.23 , pp. 115-128
    • Lian, H.1
  • 25
    • 4944222926 scopus 로고    scopus 로고
    • Functional Convex Averaging and Synchronization for Time-Warped Random Curves
    • X.Liu,, and H.G.Müller (2004), ‘Functional Convex Averaging and Synchronization for Time-Warped Random Curves’, Journal of American Statistical Association, 99, 687–699. doi: 10.1198/016214504000000999
    • (2004) Journal of American Statistical Association , vol.99 , pp. 687-699
    • Liu, X.1    Müller, H.G.2
  • 30
    • 57249114751 scopus 로고    scopus 로고
    • Pairwise Curve Synchronization for Functional Data
    • R.Tang,, and H.-G.Müller, (2008), ‘Pairwise Curve Synchronization for Functional Data’, Biometrika, 95, 875–889. doi: 10.1093/biomet/asn047
    • (2008) Biometrika , vol.95 , pp. 875-889
    • Tang, R.1    Müller, H.-G.2
  • 31
    • 84885019648 scopus 로고    scopus 로고
    • Generative Models for Functional Data Using Phase and Amplitude Separation
    • J.D.Tucker,, W.Wu,, and A.Srivastava, (2013), ‘Generative Models for Functional Data Using Phase and Amplitude Separation’, Computational Statistics and Data Analysis, 61, 50–66. doi: 10.1016/j.csda.2012.12.001
    • (2013) Computational Statistics and Data Analysis , vol.61 , pp. 50-66
    • Tucker, J.D.1    Wu, W.2    Srivastava, A.3
  • 32
    • 84899477840 scopus 로고    scopus 로고
    • Analysis of Signals Under Compositional Noise With Applications to SONAR Data
    • J.D.Tucker,, W.Wu,, and A.Srivastava, (2014), ‘Analysis of Signals Under Compositional Noise With Applications to SONAR Data’, Journal of Oceanic Engineering, 39, 318–330. doi: 10.1109/JOE.2013.2254213
    • (2014) Journal of Oceanic Engineering , vol.39 , pp. 318-330
    • Tucker, J.D.1    Wu, W.2    Srivastava, A.3
  • 33
    • 0031486902 scopus 로고    scopus 로고
    • Alignment of Curves by Dynamic Time Warping
    • K.Wang,, and T.Gasser, (1997), ‘Alignment of Curves by Dynamic Time Warping’, Annals of Statistics, 25, 1251–1276. doi: 10.1214/aos/1069362747
    • (1997) Annals of Statistics , vol.25 , pp. 1251-1276
    • Wang, K.1    Gasser, T.2
  • 34
    • 83055194741 scopus 로고    scopus 로고
    • An Information-Geometric Framework for Statistical Inferences in the Neural Spike Train Space
    • W.Wu,, and A.Srivastava, (2011), ‘An Information-Geometric Framework for Statistical Inferences in the Neural Spike Train Space’, Journal of Computational Neuroscience, 31, 725–748. doi: 10.1007/s10827-011-0336-x
    • (2011) Journal of Computational Neuroscience , vol.31 , pp. 725-748
    • Wu, W.1    Srivastava, A.2
  • 35
    • 84877762230 scopus 로고    scopus 로고
    • Estimating Summary Statistics in the Spike-Train Space
    • W.Wu,, and A.Srivastava, (2013), ‘Estimating Summary Statistics in the Spike-Train Space’, Journal of Computational Neuroscience, 34, 391–410. doi: 10.1007/s10827-012-0427-3
    • (2013) Journal of Computational Neuroscience , vol.34 , pp. 391-410
    • Wu, W.1    Srivastava, A.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.