Functional data analysis (FDA), which is a branch of statistics on modeling infinite
dimensional random vectors resided in functional spaces, has become a major research …

We consider nonparametric estimation of the mean and covariance functions for
functional/longitudinal data. Strong uniform convergence rates are developed for estimators …

We propose an extensive framework for additive regression models for correlated functional
responses, allowing for multiple partially nested or crossed functional random effects with …

Semiparametric regression is a fusion between parametric regression and nonparametric
regression that integrates low-rank penalized splines, mixed model and hierarchical …

We consider estimation of covariance matrices of stationary processes. Under a short-range
dependence condition for a wide class of nonlinear processes, it is shown that the banded …

Functional principal component analysis (FPCA) has become the most widely used
dimension reduction tool for functional data analysis. We consider functional data measured …

We propose a new methodological framework for the analysis of hierarchical functional data
when the functions at the lowest level of the hierarchy are correlated. For small data sets, our …

This paper focuses on the analysis of spatially correlated functional data. We propose a
parametric model for spatial correlation and the between-curve correlation is modeled by …

Hierarchical functional data are widely seen in complex studies where subunits are nested
within units, which in turn are nested within treatment groups. We propose a general …

We introduce a new class of functional generalized linear models, where the response is a
scalar and some of the covariates are functional. We assume that the response depends on …