A centroid classifier for functional/longitudinal data

References

• Ferraty F, Vieu P. Nonparametric functional data analysis. New York: Springer; 2006.
   Google Scholar
• Horváth L, Kokoszka P. Inference for functional data with applications. New York: Springer; 2012.
   Google Scholar
• Ramsay JO, Silverman B. Applied functional data analysis: methods and case studies. New York: Springer; 2002.
   Google Scholar
• Ramsay JO, Silverman B. Functional data analysis. New York: Springer; 2005.
   Google Scholar
• Biau G, Bunea F, Wegkamp MH. Functional classification in Hilbert spaces. IEEE Trans Inform Theor. 2005;51:2163–2172.
   Web of Science ®Google Scholar
• Biau GC, Cérou F, Guyader A. Rates of convergence of the functional k-nearest neighbor estimate. IEEE Trans Inform Theor. 2010;56:2034–2040.
   Web of Science ®Google Scholar
• CÉrou F, Guyader A. Nearest neighbor classification in infinite dimension. ESAIM Prob Statist. 2006;10:340–355.
   Google Scholar
• Ferraty F, Vieu P. Curves discrimination: a nonparametric functional approach. Comput Statist Data Anal. 2003;4:161–173.
   Google Scholar
• Alonso AM, Casado D, Romo J. Supervised classification for functional data: a weighted distance approach. Comp Statist Data Anal. 2012;56:2334–2346.
   Web of Science ®Google Scholar
• Leng X, Müller HG. Classification using functional data analysis for temporal gene expression data. Bioinformatics. 2006;22:68–76.
   PubMed Web of Science ®Google Scholar
• Araki Y, Konishi S, Kawano S. Functional logistic discrimination via regularized basis expansions. Commun Statist Theory Methods. 2009;38:2944–2957.
   Web of Science ®Google Scholar
• Rossi F, Villa N. Support vector machine for functional data classification. Neurocomputing. 2006;69:730–742.
   Web of Science ®Google Scholar
• Martín-Barragán B, Lillo RE, Romo J. Interpretable support vector machines for functional data. Eur J Oper Res. 2014;232:146–155.
   Web of Science ®Google Scholar
• Cuevas A, Febrero M, Fraiman R. Robust estimation and classification for functional data via projection-based depth notions. Comput Statist. 2007;22:481–496.
   Web of Science ®Google Scholar
• López-Pintado S, Romo J. Depth-based classification for functional data. Discrete Math Theor Comput Sci. 2006;72:103–120.
   Google Scholar
• Sguera C, Galeano P, Lillo RE. Spatial depth-based classification for functional data. TEST. 2014;23:725–750.
   Web of Science ®Google Scholar
• Cardot H, Josserand E. Horvitz-Thompson estimators for functional data: asymptotic confidence bands and optimal allocation for stratified sampling. Biometrika. 2011;98:107–118.
   Web of Science ®Google Scholar
• Carroll RJ, Delaigle A, Hall P. Unexpected properties of bandwidth choice when smoothing discrete data for constructing a functional data classifier. Ann Statist. 2013;41:2739–2767.
   PubMed Web of Science ®Google Scholar
• Hall P, Van keilegom I. Two-sample tests in functional data analysis starting from discrete data. Statist Sinica. 2007;17:1511–1531.
   Web of Science ®Google Scholar
• Wu S, Müller HG. Response-adaptive regression for longitudinal data. Biometrics. 2011;67:852–860.
   PubMed Web of Science ®Google Scholar
• Cardot H, Degras D, Josserand E. Confidence bands for Horvitz-Thompson estimators using sampled noisy functional data. Bernoulli. 2013;19:2067–2097.
   Web of Science ®Google Scholar
• Delaigle A, Hall P, Bathia N. Componentwise classification and clustering of functional data. Biometrika. 2012;99:299–313.
   Web of Science ®Google Scholar
• Yao F, Wu Y, Zou J. Probability-enhanced effective diemsnion reduction for classifying sparse functional data. Test. 2016;25:1–22.
   Web of Science ®Google Scholar
• Li C, Xiao L. Optimal design for classification of functional data. Can J Stat. 2020;48:285–307.
   Web of Science ®Google Scholar
• Zhang X, Wang JL. From sparse to dense functional data and beyond. Ann Stat. 2016;44:2281–2321.
   Web of Science ®Google Scholar
• Zhou L, Lin H, Liang H. Efficient estimation of the nonparametric mean and covariance functions for longitudinal and sparse functional data. J Am Stat Assoc. 2018;113:1550–1564.
   Web of Science ®Google Scholar
• Sharghi Ghale-Joogh H, Hosseini-Nasab SME. On mean derivative estimation of longitudinal and functional data: from sparse to dense. Stat Papers. 2020;1–20. DOI:https://doi.org/10.1007/s00362-020-01173-5
   Google Scholar
• Zhang X, Wang JL. Optimal weighting schemes for longitudinal and functional data. Stat Prob Lett. 2018;138:165–170.
   Web of Science ®Google Scholar
• Tuddenham R, Snyder M. Physical growth of California boys and girls from birth to eighteen years. Univ Calif Publ Child Dev. 1954;1:183–364.
   PubMedGoogle Scholar
• Bunea F, Ivanescu AE, Wegkamp MH. Adaptive inference for the mean of a Gaussian process in functional data. J R Stat Soc B. 2011;73:531–558.
   Google Scholar
• Cai TT, Yuan M. Optimal estimation of the mean function based on discretely sampled functional data: phase transition. Ann Statist. 2011;39:2330–2355.
   Web of Science ®Google Scholar
• Chen LH, Jiang CR. Sensible functional linear discriminant analysis. Comput Stat Data Anal. 2018;126:39–52.
   Web of Science ®Google Scholar
• James GM, Hastie TJ. Functional linear discriminant analysis for irregularly sampled curves. J R Stat Soc Ser B. 2001;63:533–550.
   Google Scholar
• Li Y, Hsing T. Uniform convergence rates for nonparametric regression and principal component analysis in functional/longitudinal data. Ann Statist. 2010;38:3321–3351.
   Web of Science ®Google Scholar
• Panaretos VM, Kraus D, Maddocks JH. Second-order comparison of Gaussian random functions and the geometry of DNA minicircles. J Amer Statist Assoc. 2010;105:670–682.
   Web of Science ®Google Scholar