B1194
Title: Intrinsic feature extraction via functional data analysis
Authors: Yuko Araki - Shizuoka University (Japan) [presenting]
Abstract: Recent years have seen that functional data analysis are capable of extracting intrinsic features from recently arising complicated and high dimensional data, such as three dimensional brain sMRI, time course microarray data , or hundreds of records of human gait, for example. We introduce statistical methods for classifying individuals with such high dimensional covariates. We have also constructed a statistical model to characterize the relationship between the event time and a set of baseline covariates. The proposed method is based on composite basis function, which is an extended version of basis expansions with the help of sparse PCA. Further, $L_1-$type penalty constraints are imposed in the estimation of the parameters of logistic discrimination and Cox proportional hazard models. This two-step regularization method accomplishes both covariates selection and estimation of unknown model parameters simultaneously. The crucial issue is how to select the regularization parameters used in model estimation. We propose to use information criterion based model selection. The proposed models are applied to real data example and Monte Carlo simulations are conducted to examine the efficiency of our modeling strategies.
