Figure 1.1 provides a prototype for the type of data that we shall consider. It shows the
heights of 10 girls measured at a set of 31 ages in the Berkeley Growth Study (Tuddenham …

With contributions from some of the most prominent researchers in the field, this carefully
edited collection provides a clear, comprehensive, and unified overview of recent …

In this article local empirical likelihood-based inference for a varying coefficient model with
longitudinal data is investigated. First, we show that the naive empirical likelihood ratio is …

Regression models for functional responses and scalar predictors are often fitted by means
of basis functions, with quadratic roughness penalties applied to avoid overfitting. The fitting …

Function‐on‐function regression refers to the situation where both independent and
dependent variables in a regression model are of functional nature. Functional concurrent …

Quantile regression problems in practice may require flexible semiparametric forms of the
predictor for modeling the dependence of responses on covariates. Furthermore, it is often …

This paper proposes an efficient online learning algorithm to track the smoothing functions of
Additive Models. The key idea is to combine the linear representation of Additive Models …

This study considers a functional concurrent hidden Markov model. The proposed model
consists of two components. One is a transition model for elucidating how potential …

This paper considers composite quantile regression (CQR) estimation and inference for
varying-coefficient models with missing covariates. We propose the weighted local linear …

Motivated by a problem in describing forest nitrogen cycling, in this paper we explore
regression models for spatial images. Specifically, we present a functional concurrent linear …