  In this article, the analysis of misspecification was extended to the
recently introduced stochastic restricted biased estimators when
multicollinearity exists among the explanatory variables. The Stochastic
Restricted Ridge Estimator (SRRE), Stochastic Restricted Almost Unbiased Ridge
Estimator (SRAURE), Stochastic Restricted Liu Estimator (SRLE), Stochastic
Restricted Almost Unbiased Liu Estimator (SRAULE), Stochastic Restricted
Principal Component Regression Estimator (SRPCR), Stochastic Restricted r-k
class estimator (SRrk) and Stochastic Restricted r-d class estimator (SRrd)
were examined in the misspecified regression model due to missing relevant
explanatory variables when incomplete prior information of the regression
coefficients is available. Further, the superiority conditions between
estimators and their respective predictors were obtained in the mean square
error matrix (MSEM) sense. Finally, a numerical example and a Monte Carlo
simulation study were used to illustrate the theoretical findings.
