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Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information

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  • Manickavasagar Kayanan
  • Pushpakanthie Wijekoon

Abstract

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 (SRPCRE), Stochastic Restricted - (SRrk) class estimator, and Stochastic Restricted - (SRrd) class estimator 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.

Suggested Citation

  • Manickavasagar Kayanan & Pushpakanthie Wijekoon, 2018. "Stochastic Restricted Biased Estimators in Misspecified Regression Model with Incomplete Prior Information," Journal of Probability and Statistics, Hindawi, vol. 2018, pages 1-8, April.
    • Handle: RePEc:hin:jnljps:1452181
    DOI: 10.1155/2018/1452181
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