Ridge Stochastic Restricted Estimators in Semiparametric Linear Measurement Error Models

In this article we consider the stochastic restricted ridge estimation in semipara-metric linear models when the covariates are measured with additive errors. The development of penalized corrected likelihood method in such model is the basis for derivation of ridge estimates. The asymptotic normality of the resulting estimates are established. Also, necessary and sufficient conditions, for the superiority of the proposed estimator over its counterpart, for selecting the ridge parameter k are obtained. A Monte Carlo simulation study is also performed to illustrate the finite sample performance of the proposed procedures. Finally theoretical results are applied to Egyptian pottery Industry data set.