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:: Volume 13, Issue 2 (2-2020) ::
JSS 2020, 13(2): 441-460 Back to browse issues page
Feasible Generalized Rdge Robust Estimator in Semiparametric Regression Models
Mahdi Roozbeh * , Morteza Amini
Abstract:   (6164 Views)

‎In many fields such as econometrics‎, ‎psychology‎, ‎social sciences‎, ‎medical sciences‎, ‎engineering‎, ‎etc.‎, ‎we face with multicollinearity among the explanatory variables and the existence of outliers in data‎. ‎In such situations‎, ‎the ordinary least-squares estimator leads to an inaccurate estimate‎. ‎The robust methods are used to handle the outliers‎. ‎Also‎, ‎to overcome multicollinearity ridge estimators are suggested‎. ‎On the other hand‎, ‎when the error terms are heteroscedastic or correlated‎, ‎the generalized least squares method is used‎. ‎In this paper‎, ‎a fast algorithm for computation of the feasible generalized least trimmed squares ridge estimator in a semiparametric regression model is proposed and then‎, ‎the performance of the proposed estimators is examined through a Monte Carlo simulation study and a real data set.

Keywords: ‎Breakdown Point‎, ‎Generalized Cross Validation‎, ‎Least Trimmed Squares Estimator‎, ‎Outliers‎, ‎Semiparametric Regression Model.
Full-Text [PDF 232 kb]   (1568 Downloads)    
Type of Study: Research | Subject: Applied Statistics
Received: 2017/10/17 | Accepted: 2018/06/29 | Published: 2019/08/16
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Roozbeh M, Amini M. Feasible Generalized Rdge Robust Estimator in Semiparametric Regression Models. JSS 2020; 13 (2) :441-460
URL: http://jss.irstat.ir/article-1-560-en.html

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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 13, Issue 2 (2-2020) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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