Stochastic Restricted Biased Estimators in misspecified regression model with incomplete prior information

摘要

In this article, the analysis of misspecification was extended to therecently introduced stochastic restricted biased estimators whenmulticollinearity exists among the explanatory variables. The StochasticRestricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased RidgeEstimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), StochasticRestricted Almost Unbiased Liu Estimator (SRAULE), Stochastic RestrictedPrincipal Component Regression Estimator (SRPCR), Stochastic Restricted r-kclass estimator (SRrk) and Stochastic Restricted r-d class estimator (SRrd)were examined in the misspecified regression model due to missing relevantexplanatory variables when incomplete prior information of the regressioncoefficients is available. Further, the superiority conditions betweenestimators and their respective predictors were obtained in the mean squareerror matrix sense (MSEM). Finally, a numerical example and a Monte Carlosimulation study were done to illustrate the theoretical findings.