Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution

Güney, Yeşim; Tuaç, Yetkin; Özdemir, Şenay; Arslan, Olçay

doi:10.1007/s00180-020-01036-5

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…For a logistic regression model, the most commonly used method is maximum likelihood estimation (MLE). However, the MLE method is very sensitive to outliers [1,6,7,12,15,25,31], therefore, it will be seriously affected, and lead to a large deviation in the prediction of classification probability. In practical applications, many covariates are introduced in the initial stage of modeling.…”

Section: Introductionmentioning

confidence: 99%

Robust variable selection in the logistic regression model

Jiang¹,

Zhang²,

MEİLAN³

2021

Hacettepe Journal of Mathematics and Statistics

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In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model. The proposed method is robust to outliers and considers the goodness-offit of the regression model. Furthermore, we apply an MM algorithm to solve the proposed optimization problem. Monte Carlo studies are evaluated the finite-sample performance of the proposed method. The results show that when there are outliers in the dataset or the distribution of covariate variable deviates from the normal distribution, the finitesample performance of the proposed method is better than that of other existing methods. Finally, the proposed methodology is applied to the data analysis of Parkinson's disease.

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Section: Introductionmentioning

confidence: 99%

Robust variable selection in the logistic regression model

Jiang¹,

Zhang²,

MEİLAN³

2021

Hacettepe Journal of Mathematics and Statistics

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Entropy techniques for robust management decision making in high-dimensional data

Kalina

2024

Serb J Management

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Entropy, a key measure of chaos or diversity, has recently found intriguing applications in the realm of management science. Traditional entropy-based approaches for data analysis, however, prove inadequate when dealing with high-dimensional datasets. In this paper, a novel uncertainty coefficient based on entropy is proposed for categorical data, together with a pattern discovery method suitable for management applications. Furthermore, we present a robust fractal-inspired technique for estimating covariance matrices in multivariate data. The efficacy of this method is thoroughly examined using three real datasets with economic relevance. The results demonstrate the superior performance of our approach, even in scenarios involving a limited number of variables. This suggests that managerial decision-making processes should reflect the inherent fractal structure present in the given multivariate data. The work emphasizes the importance of considering fractal characteristics in managerial decision-making, thereby advancing the applicability and effectiveness of entropy-based methods in management science.

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Robust estimation and variable selection in heteroscedastic regression model using least favorable distribution

Cited by 2 publications

References 30 publications

Robust variable selection in the logistic regression model

Robust variable selection in the logistic regression model

Entropy techniques for robust management decision making in high-dimensional data

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