Generalized two-parameter estimator in linear regression model

	Generalized two-parameter estimator in linear regression model
Journal of Mathematical Modeling
مقاله 4، دوره 8، شماره 2، مرداد 2020، صفحه 157-176 اصل مقاله (293.97 K)
نوع مقاله: Research Article
شناسه دیجیتال (DOI): 10.22124/jmm.2020.14903.1353
نویسنده
Amir Zeinal^*
Department of statistics, Faculty of Mathematical Sciences, University of Guilan, Rasst, Iran
چکیده
In this paper, a new two-parameter estimator is proposed. This estimator is a generalization of two-parameter (TP) estimator introduced by Ozakle and Kaciranlar (The restricted and unrestricted two-parameter estimator, Commun. Statist. Theor. Meth. 36 (2007) 2707--2725) and includes the ordinary least squares (OLS), the ridge and the generalized Liu estimators, as special cases. Here, the performance of this new estimator over the TP estimator is theoretically investigated in terms of quadratic bias (QB) criterion and its performance over the OLS and TP estimators is also studied in terms of mean squared error matrix (MSEM) criterion. Furthermore, the estimation of the biasing parameters is obtained, a numerical example is given and a simulation study is done as well.
کلیدواژه‌ها
Generalized Liu estimator؛ Lagrange method؛ mean squared error؛ ridge estimator؛ two-parameter estimator

آمار تعداد مشاهده مقاله: 743 تعداد دریافت فایل اصل مقاله: 870

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