Outlier Resistant Estimation in Difference-Based Semiparametric Partially Linear Models

Türkmen, Asuman; Tabakan, Gülin

doi:10.1080/03610918.2013.781627

Cited by 6 publications

(1 citation statement)

References 31 publications

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“…In this section, we adopted the supervisor's performance dataset to compare the performance of the proposed estimator with some of the existing estimators. The dataset was originally employed by Chatterjee and Price 37 and later by the following authors to analyzed model with multicollinearity and outliers: Turkmen and Tabakan 38 ; Abonazel and Gad 39 ; and Dawoud and Abonazel 26 . The linear regression model is defined as follows:

y_{i} = β_{1} x_{i 1} + β_{2} x_{i 2} + β_{3} x_{i 3} + β_{4} x_{i 4} + β_{5} x_{i 5} + β_{6} x_{i 6} + ε_{i}, i = 1, \dots, 30,

where

y_{i}

is the overall rating of job being done by supervisor,

x_{i 1}

denotes handles employee complaints,

x_{i 2}

denotes does not allow special privileges,

x_{i 3}

represent opportunity to learn new things,

x_{i 4}

represent raises based on performance,

x_{i 5}

denotes too critical of poor performance; and

x_{i 6}

denotes rate of advancing to better jobs.…”

Section: Empirical Applicationmentioning

confidence: 99%

Combining principal component and robust ridge estimators in linear regression model with multicollinearity and outlier

Arum

Ugwuowo

2022

Concurrency and Computation

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The method of least squared suffers a setback when there is multicollinearity and outliers in the linear regression model. In this article, we developed a new estimator to jointly handle multicollinearity and outliers by pooling the following estimators together: the M-estimator, the principal component and the ridge estimator. The new estimator is called the robust r-k estimator and is employed. We established theoretically that the new estimator is better than some of the existing ones. The simulation studies and real-life application supports the efficiency of the new method. K E Y W O R D SM-estimator, multicollinearity, outliers, principal component, ridge estimator 1 the impact of an outlier on the following: fitted values, the regression coefficient, estimated variance of or the goodness of fit statistics. Outliers are observations that posed a noticeable change on the model estimates. 16 The presence of an outlier in a model affects the efficiency of LSE. 17,18 Robust regression estimator produced more reliable and stable estimates than LSE for linear regression models with outliers. [19][20][21][22][23][24] Examples include the M-estimator that is resistant to an outlier in the y-direction. 19

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