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Functional linear model

Citations

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Cited by:

  1. Shuang Wu & Hans-Georg Müller, 2011. "Response-Adaptive Regression for Longitudinal Data," Biometrics, The International Biometric Society, vol. 67(3), pages 852-860, September.
  2. Peter Hall & Giles Hooker, 2016. "Truncated linear models for functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(3), pages 637-653, June.
  3. Imaizumi, Masaaki & Kato, Kengo, 2018. "PCA-based estimation for functional linear regression with functional responses," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 15-36.
  4. Bereswill, Mareike & Johannes, Jan, 2011. "On the effect of noisy observations of the regressor in a functional linear model," LIDAM Discussion Papers ISBA 2011039, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  5. Salish, Nazarii & Gleim, Alexander, 2019. "A moment-based notion of time dependence for functional time series," Journal of Econometrics, Elsevier, vol. 212(2), pages 377-392.
  6. Kalogridis, Ioannis & Van Aelst, Stefan, 2019. "Robust functional regression based on principal components," Journal of Multivariate Analysis, Elsevier, vol. 173(C), pages 393-415.
  7. Sam K. Hui & Tom Meyvis & Henry Assael, 2014. "Analyzing Moment-to-Moment Data Using a Bayesian Functional Linear Model: Application to TV Show Pilot Testing," Marketing Science, INFORMS, vol. 33(2), pages 222-240, March.
  8. Guillas, Serge, 2001. "Rates of convergence of autocorrelation estimates for autoregressive Hilbertian processes," Statistics & Probability Letters, Elsevier, vol. 55(3), pages 281-291, December.
  9. Siegfried Hörmann & Łukasz Kidziński, 2015. "A Note on Estimation in Hilbertian Linear Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 43-62, March.
  10. Shin, Hyejin & Hsing, Tailen, 2012. "Linear prediction in functional data analysis," Stochastic Processes and their Applications, Elsevier, vol. 122(11), pages 3680-3700.
  11. Crambes, Christophe & Hilgert, Nadine & Manrique, Tito, 2016. "Estimation of the noise covariance operator in functional linear regression with functional outputs," Statistics & Probability Letters, Elsevier, vol. 113(C), pages 7-15.
  12. Grith, Maria & Härdle, Wolfgang Karl & Kneip, Alois & Wagner, Heiko, 2016. "Functional principal component analysis for derivatives of multivariate curves," SFB 649 Discussion Papers 2016-033, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
  13. Eduardo García‐Portugués & Javier Álvarez‐Liébana & Gonzalo Álvarez‐Pérez & Wenceslao González‐Manteiga, 2021. "A goodness‐of‐fit test for the functional linear model with functional response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 502-528, June.
  14. Fabrizio Maturo & Antonio Balzanella & Tonio Di Battista, 2019. "Building Statistical Indicators of Equitable and Sustainable Well-Being in a Functional Framework," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 146(3), pages 449-471, December.
  15. Mas, André, 2006. "A sufficient condition for the CLT in the space of nuclear operators--Application to covariance of random functions," Statistics & Probability Letters, Elsevier, vol. 76(14), pages 1503-1509, August.
  16. André Mas, 2002. "Testing for the Mean of Random Curves : from Penalization to Dimension Selection," Working Papers 2002-08, Center for Research in Economics and Statistics.
  17. Yu, Ping & Song, Xinyuan & Du, Jiang, 2024. "Composite expectile estimation in partial functional linear regression model," Journal of Multivariate Analysis, Elsevier, vol. 203(C).
  18. Ping Yu & Zhongzhan Zhang & Jiang Du, 2016. "A test of linearity in partial functional linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(8), pages 953-969, November.
  19. Cardot, Hervé & Johannes, Jan, 2010. "Thresholding projection estimators in functional linear models," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 395-408, February.
  20. Febrero-Bande, Manuel & Galeano, Pedro & González-Manteiga, Wenceslao, 2019. "Estimation, imputation and prediction for the functional linear model with scalar response with responses missing at random," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 91-103.
  21. Yu, Dengdeng & Zhang, Li & Mizera, Ivan & Jiang, Bei & Kong, Linglong, 2019. "Sparse wavelet estimation in quantile regression with multiple functional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 136(C), pages 12-29.
  22. Herve Cardot & Robert Faivre & Michel Goulard, 2003. "Functional approaches for predicting land use with the temporal evolution of coarse resolution remote sensing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1185-1199.
  23. Crambes, Christophe & Gannoun, Ali & Henchiri, Yousri, 2013. "Support vector machine quantile regression approach for functional data: Simulation and application studies," Journal of Multivariate Analysis, Elsevier, vol. 121(C), pages 50-68.
  24. Sylvain Robbiano & Matthieu Saumard & Michel Curé, 2016. "Improving prediction performance of stellar parameters using functional models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(8), pages 1465-1476, June.
  25. Nerini, David & Ghattas, Badih, 2007. "Classifying densities using functional regression trees: Applications in oceanology," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4984-4993, June.
  26. Frédéric Ferraty & Ingrid Van Keilegom & Philippe Vieu, 2010. "On the Validity of the Bootstrap in Non‐Parametric Functional Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(2), pages 286-306, June.
  27. Mark J. Meyer & Brent A. Coull & Francesco Versace & Paul Cinciripini & Jeffrey S. Morris, 2015. "Bayesian function‐on‐function regression for multilevel functional data," Biometrics, The International Biometric Society, vol. 71(3), pages 563-574, September.
  28. Siegfried Hörmann & Łukasz Kidziński & Piotr Kokoszka, 2015. "Estimation in Functional Lagged Regression," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(4), pages 541-561, July.
  29. Martin A. Lindquist, 2012. "Functional Causal Mediation Analysis With an Application to Brain Connectivity," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(500), pages 1297-1309, December.
  30. Qi, Xin & Luo, Ruiyan, 2018. "Function-on-function regression with thousands of predictive curves," Journal of Multivariate Analysis, Elsevier, vol. 163(C), pages 51-66.
  31. Cardot, Herve & Crambes, Christophe & Kneip, Alois & Sarda, Pascal, 2007. "Smoothing splines estimators in functional linear regression with errors-in-variables," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4832-4848, June.
  32. Kalogridis, Ioannis, 2024. "Robust and adaptive functional logistic regression," Computational Statistics & Data Analysis, Elsevier, vol. 192(C).
  33. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
  34. Merve Yasemin Tekbudak & Marcela Alfaro-Córdoba & Arnab Maity & Ana-Maria Staicu, 2019. "A comparison of testing methods in scalar-on-function regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 103(3), pages 411-436, September.
  35. Ruiyuan Cao & Jiang Du & Jianjun Zhou & Tianfa Xie, 2020. "FPCA-based estimation for generalized functional partially linear models," Statistical Papers, Springer, vol. 61(6), pages 2715-2735, December.
  36. Nerini, David & Monestiez, Pascal & Manté, Claude, 2010. "Cokriging for spatial functional data," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 409-418, February.
  37. repec:cte:wsrepe:ws133228 is not listed on IDEAS
  38. Laurent Delsol, 2013. "No effect tests in regression on functional variable and some applications to spectrometric studies," Computational Statistics, Springer, vol. 28(4), pages 1775-1811, August.
  39. Qi, Xin & Zhao, Hongyu, 2011. "Some theoretical properties of Silverman's method for Smoothed functional principal component analysis," Journal of Multivariate Analysis, Elsevier, vol. 102(4), pages 741-767, April.
  40. Ping Yu & Zhongyi Zhu & Zhongzhan Zhang, 2019. "Robust exponential squared loss-based estimation in semi-functional linear regression models," Computational Statistics, Springer, vol. 34(2), pages 503-525, June.
  41. Yousri Slaoui, 2020. "Recursive nonparametric regression estimation for dependent strong mixing functional data," Statistical Inference for Stochastic Processes, Springer, vol. 23(3), pages 665-697, October.
  42. Jianjun Zhou & Zhao Chen & Qingyan Peng, 2016. "Polynomial spline estimation for partial functional linear regression models," Computational Statistics, Springer, vol. 31(3), pages 1107-1129, September.
  43. Hlubinka, Daniel & Prchal, Lubos, 2007. "Changes in atmospheric radiation from the statistical point of view," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4926-4941, June.
  44. Zhu, Hanbing & Zhang, Riquan & Yu, Zhou & Lian, Heng & Liu, Yanghui, 2019. "Estimation and testing for partially functional linear errors-in-variables models," Journal of Multivariate Analysis, Elsevier, vol. 170(C), pages 296-314.
  45. Melody Denhere & Huybrechts F. Bindele, 2016. "Rank estimation for the functional linear model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(10), pages 1928-1944, August.
  46. Ming‐Yueh Huang & Kwun Chuen Gary Chan, 2024. "Gradient‐based approach to sufficient dimension reduction with functional or longitudinal covariates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 51(4), pages 1567-1586, December.
  47. Marine Carrasco & Jean-Pierre Florens, 2000. "Efficient GMM Estimation Using the Empirical Characteristic Function," Working Papers 2000-33, Center for Research in Economics and Statistics.
  48. Han Shang, 2014. "A survey of functional principal component analysis," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(2), pages 121-142, April.
  49. Kneip, Alois & Crambes, Christophe & Cardot, Herve & Sarda, Pascal, 2006. "Smoothing Splines Estimators in Functional Linear Regression with Errors-in-Variables," Bonn Econ Discussion Papers 2/2006, University of Bonn, Bonn Graduate School of Economics (BGSE).
  50. Hervé Cardot & Frédéric Ferraty & André Mas & Pascal Sarda, 2003. "Testing Hypotheses in the Functional Linear Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 241-255, March.
  51. Gregorutti, Baptiste & Michel, Bertrand & Saint-Pierre, Philippe, 2015. "Grouped variable importance with random forests and application to multiple functional data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 15-35.
  52. Comte , Fabienne & Johannes, Jan, 2011. "Adaptive functional linear regression," LIDAM Discussion Papers ISBA 2011038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
  53. Hao Ji & Hans-Georg Müller, 2017. "Optimal designs for longitudinal and functional data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(3), pages 859-876, June.
  54. González-Rodríguez, Gil & Colubi, Ana, 2017. "On the consistency of bootstrap methods in separable Hilbert spaces," Econometrics and Statistics, Elsevier, vol. 1(C), pages 118-127.
  55. Goia, Aldo & May, Caterina & Fusai, Gianluca, 2010. "Functional clustering and linear regression for peak load forecasting," International Journal of Forecasting, Elsevier, vol. 26(4), pages 700-711, October.
  56. Angelina Roche, 2018. "Local optimization of black-box functions with high or infinite-dimensional inputs: application to nuclear safety," Computational Statistics, Springer, vol. 33(1), pages 467-485, March.
  57. Febrero-Bande, Manuel & Galeano, Pedro & González-Manteiga, Wenceslao, 2010. "Measures of influence for the functional linear model with scalar response," Journal of Multivariate Analysis, Elsevier, vol. 101(2), pages 327-339, February.
  58. Mareike Bereswill & Jan Johannes, 2013. "On the effect of noisy measurements of the regressor in functional linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(3), pages 488-513, September.
  59. Daniel R. Kowal & David S. Matteson & David Ruppert, 2019. "Functional Autoregression for Sparsely Sampled Data," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 37(1), pages 97-109, January.
  60. Yousri Slaoui, 2021. "Recursive non-parametric kernel classification rule estimation for independent functional data," Computational Statistics, Springer, vol. 36(1), pages 79-112, March.
  61. Mousavi, Seyed Nourollah & Sørensen, Helle, 2017. "Multinomial functional regression with wavelets and LASSO penalization," Econometrics and Statistics, Elsevier, vol. 1(C), pages 150-166.
  62. Cuevas, Antonio & Febrero, Manuel & Fraiman, Ricardo, 2004. "An anova test for functional data," Computational Statistics & Data Analysis, Elsevier, vol. 47(1), pages 111-122, August.
  63. Kalogridis, Ioannis & Van Aelst, Stefan, 2023. "Robust penalized estimators for functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
  64. Ding, Hui & Liu, Yanghui & Xu, Wenchao & Zhang, Riquan, 2017. "A class of functional partially linear single-index models," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 68-82.
  65. Lakraj, Gamage Pemantha & Ruymgaart, Frits, 2017. "Some asymptotic theory for Silverman’s smoothed functional principal components in an abstract Hilbert space," Journal of Multivariate Analysis, Elsevier, vol. 155(C), pages 122-132.
  66. Cui, Xia & Lin, Hongmei & Lian, Heng, 2020. "Partially functional linear regression in reproducing kernel Hilbert spaces," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
  67. Cardot, Hervé & Sarda, Pacal, 2005. "Estimation in generalized linear models for functional data via penalized likelihood," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 24-41, January.
  68. Yue Wang & Joseph G. Ibrahim & Hongtu Zhu, 2020. "Partial least squares for functional joint models with applications to the Alzheimer's disease neuroimaging initiative study," Biometrics, The International Biometric Society, vol. 76(4), pages 1109-1119, December.
  69. Fermanian, Adeline, 2022. "Functional linear regression with truncated signatures," Journal of Multivariate Analysis, Elsevier, vol. 192(C).
  70. Ana M. Aguilera & Manuel Escabias & Francisco A. Ocaña & Mariano J. Valderrama, 2015. "Functional Wavelet-Based Modelling of Dependence Between Lupus and Stress," Methodology and Computing in Applied Probability, Springer, vol. 17(4), pages 1015-1028, December.
  71. Chen, Xuerong & Li, Haoqi & Liang, Hua & Lin, Huazhen, 2019. "Functional response regression analysis," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 218-233.
  72. Andrii Babii & Marine Carrasco & Idriss Tsafack, 2024. "Functional Partial Least-Squares: Optimal Rates and Adaptation," Papers 2402.11134, arXiv.org.
  73. repec:hum:wpaper:sfb649dp2016-033 is not listed on IDEAS
  74. Hörmann, Siegfried & Jammoul, Fatima, 2023. "Prediction in functional regression with discretely observed and noisy covariates," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
  75. Brunel, Élodie & Mas, André & Roche, Angelina, 2016. "Non-asymptotic adaptive prediction in functional linear models," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 208-232.
  76. Zhou, Jianjun & Chen, Min, 2012. "Spline estimators for semi-functional linear model," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 505-513.
  77. O. I. Traore & P. Cristini & N. Favretto-Cristini & L. Pantera & P. Vieu & S. Viguier-Pla, 2019. "Clustering acoustic emission signals by mixing two stages dimension reduction and nonparametric approaches," Computational Statistics, Springer, vol. 34(2), pages 631-652, June.
  78. Geenens, Gery, 2015. "Moments, errors, asymptotic normality and large deviation principle in nonparametric functional regression," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 369-377.
  79. Yao, Fang, 2007. "Asymptotic distributions of nonparametric regression estimators for longitudinal or functional data," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 40-56, January.
  80. F. Ferraty & A. Goia & E. Salinelli & P. Vieu, 2013. "Functional projection pursuit regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 293-320, June.
  81. Cristian Preda & Gilbert Saporta & Caroline Lévéder, 2007. "PLS classification of functional data," Computational Statistics, Springer, vol. 22(2), pages 223-235, July.
  82. Lin, Hongmei & Jiang, Xuejun & Lian, Heng & Zhang, Weiping, 2019. "Reduced rank modeling for functional regression with functional responses," Journal of Multivariate Analysis, Elsevier, vol. 169(C), pages 205-217.
  83. Shuzhi Zhu & Peixin Zhao, 2019. "Tests for the linear hypothesis in semi-functional partial linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 82(2), pages 125-148, March.
  84. Frédéric Ferraty & Philippe Vieu, 2002. "The Functional Nonparametric Model and Application to Spectrometric Data," Computational Statistics, Springer, vol. 17(4), pages 545-564, December.
  85. Lee, Eun Ryung & Park, Byeong U., 2012. "Sparse estimation in functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 1-17.
  86. Guochang Wang & Xiang-Nan Feng & Min Chen, 2016. "Functional Partial Linear Single-index Model," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 261-274, March.
  87. Qing-Yan Peng & Jian-Jun Zhou & Nian-Sheng Tang, 2016. "Varying coefficient partially functional linear regression models," Statistical Papers, Springer, vol. 57(3), pages 827-841, September.
  88. Martínez-Hernández, Israel & Genton, Marc G. & González-Farías, Graciela, 2019. "Robust depth-based estimation of the functional autoregressive model," Computational Statistics & Data Analysis, Elsevier, vol. 131(C), pages 66-79.
  89. Ahn, Kyungmin & Tucker, J. Derek & Wu, Wei & Srivastava, Anuj, 2020. "Regression models using shapes of functions as predictors," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
  90. Said Attaoui & Nengxiang Ling, 2016. "Asymptotic results of a nonparametric conditional cumulative distribution estimator in the single functional index modeling for time series data with applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 79(5), pages 485-511, July.
  91. Lian, Heng & Choi, Taeryon & Meng, Jie & Jo, Seongil, 2016. "Posterior convergence for Bayesian functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 27-41.
  92. Julien Jacques & Cristian Preda, 2014. "Functional data clustering: a survey," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(3), pages 231-255, September.
  93. Yu-Ru Su & Chong-Zhi Di & Li Hsu, 2017. "Hypothesis testing in functional linear models," Biometrics, The International Biometric Society, vol. 73(2), pages 551-561, June.
  94. Luo, Ruiyan & Qi, Xin, 2015. "Sparse wavelet regression with multiple predictive curves," Journal of Multivariate Analysis, Elsevier, vol. 134(C), pages 33-49.
  95. Dalia Valencia & Rosa E. Lillo & Juan Romo, 2019. "A Kendall correlation coefficient between functional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1083-1103, December.
  96. Jonathan E. Gellar & Elizabeth Colantuoni & Dale M. Needham & Ciprian M. Crainiceanu, 2014. "Variable-Domain Functional Regression for Modeling ICU Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1425-1439, December.
  97. Philip T. Reiss & Jeff Goldsmith & Han Lin Shang & R. Todd Ogden, 2017. "Methods for Scalar-on-Function Regression," International Statistical Review, International Statistical Institute, vol. 85(2), pages 228-249, August.
  98. Amato, U. & Antoniadis, A. & De Feis, I., 2006. "Dimension reduction in functional regression with applications," Computational Statistics & Data Analysis, Elsevier, vol. 50(9), pages 2422-2446, May.
  99. Zhu, Hanbing & Li, Rui & Zhang, Riquan & Lian, Heng, 2020. "Nonlinear functional canonical correlation analysis via distance covariance," Journal of Multivariate Analysis, Elsevier, vol. 180(C).
  100. repec:eca:wpaper:2013/131191 is not listed on IDEAS
  101. Christophe Crambes & Ali Gannoun & Yousri Henchiri, 2014. "Modelling functional additive quantile regression using support vector machines approach," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 26(4), pages 639-668, December.
  102. Manuel Febrero-Bande & Pedro Galeano & Wenceslao González-Manteiga, 2017. "Functional Principal Component Regression and Functional Partial Least-squares Regression: An Overview and a Comparative Study," International Statistical Review, International Statistical Institute, vol. 85(1), pages 61-83, April.
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