The properties of automatic model selection are discussed, focusing on PcGets. We explain the background concepts and why automatic methods can perform well. Criticisms of model selection procedures are noted and rebutted. The algorithm is sketched, distinguishing between costs of search and...

In this paper, we investigate several variable selection procedures to give an overview of the existing literature for practitioners. 'Let the data speak for themselves' has become the motto of many applied researchers since the number of data has significantly grown. Automatic model selection...

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...

An efficient estimating equations procedure is developed for performing variable selection and defining semiparametric efficient estimates simultaneously for the heteroscedastic partially linear single-index model. The estimating equations are proposed based on the smooth threshold estimating...

In this paper, a new robust and efficient estimation approach based on local modal regression is proposed for partially linear single-index models, of which the univariate nonparametric link function is approximated by local polynomial regression. The asymptotic normality of proposed estimators...