Title: Functional path analysis with composite basis expansions
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 describing the direct and indirect dependencies among a set of variables including high dimensional covariates. The proposed method is based on composite basis function, which is an extended version of basis expansions with the help of sparse PCA. The proposed models are applied to real data example and Monte Carlo simulations are conducted to examine the efficiency of our modelling strategies.