Functional principal component analysis (FPCA) based on theKarhunen-Lo`eve decomposition has been successfully applied in manyapplications, mainly for one sample problems. In this paper we consider common functional principal components for two sample problems. Our research is motivated not only...

Functional data analysis (FDA) has become a popular technique in applied statistics.In particular, this methodology has received considerable attention in recent studiesin empirical finance. In this talk we discuss selected topics of functional principalcomponents analysis that are motivated by...

Our paper introduces a new estimation method for arbitrary temporal heterogeneityin panel data models. The paper provides a semiparametric method for estimatinggeneral patterns of cross-sectional specific time trends. The methods proposed in thepaper are related to principal component analysis...

This work deals with a generalization of the Total Least Squaresmethod in the context of the functional linear model. We first propose asmoothing splines estimator of the functional coefficient of the model withoutnoise in the covariates and we obtain an asymptotic result for this...

Non-parametric data envelopment analysis (DEA) estimators based on linear programmingmethods have been widely applied in analyses of productive efficiency. Thedistributions of these estimators remain unknown except in the simple case of one inputand one output, and previous bootstrap methods...