It's been over a decade since the first edition of Measurement Error in Nonlinear Models
splashed onto the scene, and research in the field has certainly not cooled in the interim. In …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

In the last ten years, there has been increasing interest and activity in the general area of
partially linear regression smoothing in statistics. Many methods and techniques have been …

Varying-coefficient partially linear models are frequently used in statistical modelling, but
their estimation and inference have not been systematically studied. This paper proposes a …

In this paper, we are concerned with how to select significant variables in semiparametric
modeling. Variable selection for semiparametric regression models consists of two …

This article focuses on variable selection for partially linear models when the covariates are
measured with additive errors. We propose two classes of variable selection procedures …

Partially linear models provide a useful class of tools for modeling complex data by naturally
incorporating a combination of linear and nonlinear effects within one framework. One key …

We consider partially linear models of the form Y= X Tβ+ ν (Z)+ ɛ when the response
variable Y is sometimes missing with missingness probability π depending on (X, Z), and the …

Missing covariate data are common in epidemiologic studies and disease prevention trials.
In this article regression parameter estimation in the Cox proportional hazards model is …

We consider the problem of estimating quantile regression coefficients in errors-in-variables
models. When the error variables for both the response and the manifest variables have a …