Selecting the Number of Principal Components in Functional Data.

ABSTRACT: Functional principal component analysis (FPCA) has become the most widely used dimension reduction tool for functional data analysis. We consider functional data measured at random, subject-specific time points, contaminated with measurement error, allowing for both sparse and dense functional data, and propose novel information criteria to select the number of principal component in such data. We propose a Bayesian information criterion based on marginal modeling that can consistently select the number of principal components for both sparse and dense functional data. For dense functional data, we also developed an Akaike information criterion (AIC) based on the expected Kullback-Leibler information under a Gaussian assumption. In connecting with factor analysis in multivariate time serie

SUBMITTER: Li Y 

PROVIDER: S-EPMC3872138 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature