An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …

Partially linear additive quantile regression in ultra-high dimension Page 1 The Annals of
Statistics 2016, Vol. 44, No. 1, 288–317 DOI: 10.1214/15-AOS1367 © Institute of …

We investigate a class of partially linear functional additive models (PLFAM) that predicts a
scalar response by both parametric effects of a multivariate predictor and nonparametric …

In this paper, a class of partially linear additive spatial autoregressive models (PLASARM) is
studied. With the nonparametric functions approximated by basis functions, we propose a …

A new estimation procedure based on the composite quantile regression is proposed for the
semiparametric additive partial linear models, of which the nonparametric components are …

This paper considers the problem of variable selection in regression models in the case of
functional variables that may be mixed with other type of variables (scalar, multivariate …

This paper focuses on studying the estimation method of partially linear additive spatial
autoregressive model (PLASARM) by combining both parametric and nonparametric terms …

Determining which covariates enter the linear part of a partially linear additive model is
always challenging. It is more serious when the number of covariates diverges with the …

Generalized additive models (GAMs) are a well-established statistical tool for modeling
complex nonlinear relationships between covariates and a response assumed to have a …

Simultaneous variable selection and estimation in semiparametric modeling of longitudinal/clustered
data Page 1 Bernoulli 19(1), 2013, 252–274 DOI: 10.3150/11-BEJ386 Simultaneous variable …