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https://doi.org/10.1080/03610928108828027

has been cited by the following article:

• TITLE: Performance of Existing Biased Estimators and the Respective Predictors in a Misspecified Linear Regression Model

  AUTHORS: Manickavasagar Kayanan, Pushpakanthie Wijekoon

  KEYWORDS: Misspecified Regression Model, Generalized Biased Estimator, Generalized Predictor, Mean Square Error Matrix, Scalar Mean Square Error

  JOURNAL NAME: Open Journal of Statistics, Vol.7 No.5, October 31, 2017

  ABSTRACT: In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.