With the advance of modern technology, more and more data are being recorded
continuously during a time interval or intermittently at several discrete time points. These are …

In many application areas, data are collected on a count or binary response with spatial
covariate information. In this article, we introduce a new class of generalized geoadditive …

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 …

The generalized partially linear additive model (GPLAM) is a flexible and interpretable
approach to building predictive models. It combines features in an additive manner, allowing …

This paper concerns statistical inference for longitudinal data with ultrahigh dimensional
covariates. We first study the problem of constructing confidence intervals and hypothesis …

Although various distributed machine learning schemes have been proposed recently for
purely linear models and fully nonparametric models, little attention has been paid to …

In this paper, we propose a new identifiability condition by using the logarithmic calibration
for the multiplicative distortion partial linear measurement errors models, when neither the …

In this article, we introduce a new class of nonparametric regression models, called
generalized spatially varying coefficient models (GSVCMs), for data distributed over …

This paper considers partial linear regression models when neither the response variable
nor the covariates can be directly observed, but are instead measured with both …

In this paper we consider partial linear regression models when all the variables are
measured with multiplicative distortion measurement errors. To eliminate the effect caused …