title: Estimating the covariance function with functional data.
abstract: This paper describes a two-step procedure for estimating the covariance function and its eigenvalues and eigenfunctions in situations where the data are curves or functions. The first step produces initial estimates of eigenfunctions using a standard principal components analysis. At the second step, these initial estimates are smoothed via local polynomial fitting, with the bandwidth in the kernel function being selected by a data-driven procedure. The results of a simulation study and three real examples are presented to illustrate the performance of the proposed methodology.
authors.affiliation: Department of Statistics, The Chinese University of Hong Kong, Hong Kong. sylee@sparc2.sta.cuhk.edu.hk
authors.forename: Sik-Yum
authors.initials: SY
authors.lastname: Lee
authors.forename: Wenyang
authors.initials: W
authors.lastname: Zhang
authors.forename: Xin-Yuan
authors.initials: XY
authors.lastname: Song
mesh_terms: D006801:Humans; D008960:Models, Psychological; D012919:Social Behavior; D013663:Teaching / Q000379:methods*