Article ID Journal Published Year Pages File Type
1146210 Journal of Multivariate Analysis 2013 10 Pages PDF
Abstract

In a partial linear model, some non-stochastic linear restrictions are imposed under a multicollinearity setting. Semiparametric ridge and non-ridge type estimators, in a restricted manifold are defined. For practical use, it is assumed that the covariance matrix of the error term is unknown and thus feasible estimators are replaced and their asymptotic distributional properties are derived. Also, necessary and sufficient conditions, for the superiority of the ridge type estimator over its counterpart, for selecting the ridge parameter kk are obtained. Lastly, a Monte Carlo simulation study is conducted to estimate the parametric and non-parametric parts. In this regard, kernel smoothing and cross validation methods for estimating the non-parametric function are used.

Related Topics
Physical Sciences and Engineering Mathematics Numerical Analysis
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