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Ozlem Alpu ;Revista Colombiana de Estadística 2015, 38 2

Autor: Betül Kan-Kilinç

Fuente: http://www.redalyc.org/


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Revista Colombiana de Estadística ISSN: 0120-1751 revcoles_fcbog@unal.edu.co Universidad Nacional de Colombia Colombia Kan-Kilinç, Betül; Alpu, Ozlem Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application Revista Colombiana de Estadística, vol.
38, núm.
2, julio, 2015, pp.
485-502 Universidad Nacional de Colombia Bogotá, Colombia Available in: http:--www.redalyc.org-articulo.oa?id=89940050011 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Revista Colombiana de Estadística July 2015, Volume 38, Issue 2, pp.
485 to 502 DOI: http:--dx.doi.org-10.15446-rce.v38n2.51675 Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application Combinación de algunos métodos de estimación sesgados con regresión de mínimos cuadrados recortados y su aplicación Betül Kan-Kilinç1,a , Ozlem Alpu2,b 1 Department of Statistics, Science Faculty, Anadolu University, Eskisehir, Turkey 2 Department of Statistics, Faculty of Arts and Sciences, Eskisehir Osmangazi University, Eskisehir, Turkey Abstract In the case of multicollinearity and outliers in regression analysis, the researchers are encouraged to deal with two problems simultaneously.
Biased methods based on robust estimators are useful for estimating the regression coefficients for such cases.
In this study we examine some robust biased estimators on the datasets with outliers in x direction and outliers in both x and y direction from literature by means of the R package ltsbase.
Instead of a complete data analysis, robust biased estimators are evaluated using capabilities and features of this package. Key words: Biased Estimator, Least Trimmed Squares, Robust Estimation. Resumen En el caso de multicol...





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