Estimating the covariance function with functional data.
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.
Lee
Sik-Yum
SY
Department of Statistics, The Chinese University of Hong Kong, Hong Kong. sylee@sparc2.sta.cuhk.edu.hk
Zhang
Wenyang
W
Song
Xin-Yuan
XY
D006801:Humans; D008960:Models, Psychological; D012919:Social Behavior; D013663:Teaching / Q000379:methods*