AJA Command and Staff University Journal of Systems Oriented Operations Research 2783-1493 1 1 2025 05 22 The extended two-type parameter estimator in linear regression model The extended two-type parameter estimator in linear regression model 240077 10.22034/jmor.2024.240077 EN Zeinal Amir Journal Article 2020 09 09 In this paper, a new two-type parameter estimator is introduced. This estimator is an extension of the two-parameter estimator presented by Özkale and Kaçiranlar [10], which includes the ordinary least squares, the generalized ridge and the generalized Liu estimators, as special cases. Here the performance of this new estimator over the ordinary least squares and two-parameter estimators is , theoretically, evaluated in terms of quadratic bias (QB) and mean squared error matrix (MSEM) criteria, and the optimal biasing parameters are obtained to minimize the scalar mean squared error (MSE). Then a numerical example is given and a simulation study is done to illustrate the theoretical results of the paper In this paper, a new two-type parameter estimator is introduced. This estimator is an extension of the two-parameter estimator presented by Özkale and Kaçiranlar [10], which includes the ordinary least squares, the generalized ridge and the generalized Liu estimators, as special cases. Here the performance of this new estimator over the ordinary least squares and two-parameter estimators is , theoretically, evaluated in terms of quadratic bias (QB) and mean squared error matrix (MSEM) criteria, and the optimal biasing parameters are obtained to minimize the scalar mean squared error (MSE). Then a numerical example is given and a simulation study is done to illustrate the theoretical results of the paper https://www.jmor.ir/article_240077_703716159aea1061fbd66143a182ab72.pdf