Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application

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1. Verfasser: Betül Kan-Kilinç
Format: Artículo científico
Sprache:en
Veröffentlicht: Universidad Nacional de Colombia 2015
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author Betül Kan-Kilinç
author_facet Betül Kan-Kilinç
contents Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application Betül Kan-Kilinç Ozlem Alpu Física, Astronomía y Matemáticas Biased Estimator Robust Estimation Least Trimmed Squares 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. 2015 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89940050011 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.38
format Artículo científico
id redalyc_89940050011
institution Redalyc
language en
publishDate 2015
publisher Universidad Nacional de Colombia
spellingShingle Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application
Betül Kan-Kilinç
Física, Astronomía y Matemáticas
Biased Estimator
Robust Estimation
Least Trimmed Squares
Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application Betül Kan-Kilinç Ozlem Alpu Física, Astronomía y Matemáticas Biased Estimator Robust Estimation Least Trimmed Squares 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. 2015 artículo científico 0120-1751 https://www.redalyc.org/articulo.oa?id=89940050011 en http://www.redalyc.org/revista.oa?id=899 Revista Colombiana de Estadística application/pdf Universidad Nacional de Colombia Revista Colombiana de Estadística (Colombia) Num.2 Vol.38
title Combining Some Biased Estimation Methods with Least Trimmed Squares Regression and its Application
topic Física, Astronomía y Matemáticas
Biased Estimator
Robust Estimation
Least Trimmed Squares
url https://www.redalyc.org/articulo.oa?id=89940050011