The authors study a heteroscedastic partially linear regression model and develop an inferential procedure for it. This includes a test of heteroscedasticity, a two-step estimator of the heteroscedastic variance function, semiparametric generalized least-squares estimators of the parametric and...

We consider inference for a semiparametric regression model where some covariates are measured with errors, and the errors in both the regression model and the mismeasured covariates are serially correlated. We propose a weighted estimating equations-based estimator (WEEBE) for the regression...

The growth curve model is a useful tool for studying the growth problems, repeated measurements and longitudinal data. A key point using the growth curve model to fit data is determining the degree of polynomial profile form, choosing suitable explanatory variables, shrinking some regression...

In this paper, we are concerned with the estimating problem of functional coefficient regression models with generated covariates. A new local polynomial estimation is proposed, which is based on error covariance matrix correction. It is shown that the resulting estimators are consistent,...

This paper is concerned with the estimating problem of the partially linear regression models where the linear covariates are measured with additive errors. A difference based estimation is proposed to estimate the parametric component. We show that the resulting estimator is asymptotically...

type="main" xml:id="sjos12067-abs-0001" <title type="main">Abstract</title>We study estimation and hypothesis testing in single-index panel data models with individual effects. Through regressing the individual effects on the covariates linearly, we convert the estimation problem in single-index panel data models to that...

type="main" xml:id="jtsa12077-abs-0001"This paper is concerned with the regression coefficient and autoregressive order shrinkage and selection via the smoothly clipped absolute deviation (SCAD) penalty for a partially linear model with time-series errors. By combining the profile...

The semilinear in-slide models (SLIMs) have been shown to be effective methods for normalizing microarray data [J. Fan, P. Tam, G. Vande Woude, Y. Ren, Normalization and analysis of cDNA micro-arrays using within-array replications applied to neuroblastoma cell response to a cytokine,...