Robust ridge and robust Liu estimator for regression based on the LTS estimator

Kan, Betül; Alpu, Ozlem; Yazıcı, Berna

doi:10.1080/02664763.2012.750285

Cited by 25 publications

(15 citation statements)

References 20 publications

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“…For more on the Liu [6] estimator, we refer our readers to Akdeniz and Kaçiranlar [7]; Liu [22]; Alheety and Kibria [23]; Liu [24]; Li and Yang [25]; Kan et al [26]; and very recently, Farghali [27], among others.…”

Section: Liu Estimatormentioning

confidence: 99%

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

2020

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e ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. is paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. eory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper.

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Section: Liu Estimatormentioning

confidence: 99%

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

2020

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“…Power was well below the method described in section 3, so this approach was abandoned. The robust analog of Liu's estimator, derived by Kan et al (2013), suffered from the same problem. This is not to suggest that this estimator be abandoned.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous methods have been proposed and compared regarding how this issue might be addressed (e.g., Adegoke et al 2016;Arslan Billor, 2000;Ertaa et al, 2017;Kan et al, 2013;Lukman et al, 2014;Samkar & Alpu, 2010;Kan et al, 2013). The focus has been on minimizing mean squared error.…”

Section: Introductionmentioning

confidence: 99%

Inferences Based on Robust Regression Estimators When There Is Multicolinearity

Wilcox¹

2018

ASSRJ

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The paper deals with the goal of testing hypotheses about the slope parameters of a linear regression model when there is multicolinearity. A heteroscedastic method was recently derived based on a ridge estimator, but it does not guard against the deleterious impact of outliers. Several robust analogs of the ridge estimator have been proposed that might deal with this concern. The goal here is to find a robust method that performs reasonably well in simulations.

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“…For this purpose, Kan, Alpu & Yazici (2013) studied the effectiveness of some robust biased estimators via a simulation study for different types of outliers. Also they provided a dataset with outliers in y direction to show the performance of biased estimators based on LTS.…”

Section: Contributions To Lts In the Presence Of Multicollinearitymentioning

confidence: 99%

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

Kan-Kilinç

Alpu

2015

Rev. colomb. estad.

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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. ResumenEn el caso de multicolinealidad y outliers en análisis de regresión, los investigadores se enfrentan a tener que tratar dos problemas de manera simultánea. Métodos sesgados basados en estimadores robustos son útiles para estimar los coeficientes de regresión en estos casos. En este estudio se examinan algunos estimadores sesgados robustos en conjuntos de datos con outliers en x y outliers tanto en x como en y por medio del paquete ltsbase de R. En lugar de un análisis de datos completos, los estimadores sesgados robustos son evaluados usando las capacidades y características de este paquete.Palabras clave: estimadores sesgados, mínimos cuadrados recortados, robusta estimación.a Professor.

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Robust ridge and robust Liu estimator for regression based on the LTS estimator

Cited by 25 publications

References 20 publications

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

Inferences Based on Robust Regression Estimators When There Is Multicolinearity

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

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