We consider the fixed effects panel data single-index model. For estimation of the link function and the index parameter, the local linear smoothing and the least squares method are used. We also propose a test for the presence of the fixed effects. Finite sample performances are illustrated...

In this paper, we present an estimation approach based on generalized estimating equations and a variable selection procedure for single-index models when the observed data are clustered. Unlike the case of independent observations, bias-correction is necessary when general working correlation...

In this paper, we consider the partially linear single-index models with longitudinal data. We propose the bias-corrected quadratic inference function (QIF) method to estimate the parameters in the model by accounting for the within-subject correlation. Asymptotic properties for the proposed...

We propose the penalized estimator with the smoothly clipped absolute deviation (SCAD) penalty for varying coefficient time series models, which in autoregressive models actually performs lag order selection. Theoretical properties are established. Some numerical examples are also presented.

Generalized varying coefficient partially linear models are a flexible class of semiparametric models that deal with data with different types of responses. In this paper, we focus on polynomial spline estimator as a computationally easier alternative to the more commonly used local polynomial...

Separation of the linear and nonlinear components in additive models based on penalized likelihood has received attention recently. However, it remains unknown whether consistent separation is possible in generalized additive models, and how high dimensionality is allowed. In this article, we...

type="main" xml:id="rssb12066-abs-0001" <title type="main">Summary</title> <p>We consider heteroscedastic regression models where the mean function is a partially linear single-index model and the variance function depends on a generalized partially linear single-index model. We do not insist that the variance function...</p>