Optimal stochastic restricted logistic estimator

Varathan, Nagarajah; Wijekoon, Pushpakanthie

doi:10.1007/s00362-019-01121-y

Cited by 3 publications

(1 citation statement)

References 25 publications

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“…Wu and Asar [28] derived on almost unbiased ridge logistic estimator for the binary logistic regression model. Varathan and Wijekoon [26] introduced a stochastic restricted logistic estimator. Månsson and Shukur [13] proposed a Poisson ridge regression estimator to overcome the problem of multicollinearity.…”

Section: /V Ar(y I )mentioning

confidence: 99%

The r-k class estimator in generalized linear models applicable with simulation and empirical study using a Poisson and Gamma responses

Abbasi

Özkale

2021

Hacettepe Journal of Mathematics and Statistics

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Multicollinearity is considered to be a significant problem in the estimation of parameters not only in general linear models, but also in generalized linear models (GLMs). Thus, in order to alleviate the serious effects of multicollinearity a new estimator is proposed by combining the ridge and PCR estimators in GLMs. This new estimator is called the r-k class estimator in GLMs. The various comparisons of the new estimator are made with already existing estimators in the literature, which are maximum likelihood (ML) estimator, ridge and PCR estimators, respectively. The comparisons are to be made in terms of scalar MSE criterion. So that, a numerical example and application through simulation are mentioned in the study for Poisson and Gamma response variables, respectively. On the basis of results it is found that, the proposed estimator outperforms all of its competitors comprehensively.

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Section: /V Ar(y I )mentioning

confidence: 99%

The r-k class estimator in generalized linear models applicable with simulation and empirical study using a Poisson and Gamma responses

Abbasi

Özkale

2021

Hacettepe Journal of Mathematics and Statistics

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Restricted Liu estimator under stochastic linear restrictions in generalized linear models: theory and applications

Abbasi,

Özkale

2023

Communications in Statistics - Simulation and Computation

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Penalized logistic regression with prior information for microarray gene expression classification

Genç

2022

The International Journal of Biostatistics

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Cancer classification and gene selection are important applications in DNA microarray gene expression data analysis. Since DNA microarray data suffers from the high-dimensionality problem, automatic gene selection methods are used to enhance the classification performance of expert classifier systems. In this paper, a new penalized logistic regression method that performs simultaneous gene coefficient estimation and variable selection in DNA microarray data is discussed. The method employs prior information about the gene coefficients to improve the classification accuracy of the underlying model. The coordinate descent algorithm with screening rules is given to obtain the gene coefficient estimates of the proposed method efficiently. The performance of the method is examined on five high-dimensional cancer classification datasets using the area under the curve, the number of selected genes, misclassification rate and F-score measures. The real data analysis results indicate that the proposed method achieves a good cancer classification performance with a small misclassification rate, large area under the curve and F-score by trading off some sparsity level of the underlying model. Hence, the proposed method can be seen as a reliable penalized logistic regression method in the scope of high-dimensional cancer classification.

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Optimal stochastic restricted logistic estimator

Cited by 3 publications

References 25 publications

The r-k class estimator in generalized linear models applicable with simulation and empirical study using a Poisson and Gamma responses

The r-k class estimator in generalized linear models applicable with simulation and empirical study using a Poisson and Gamma responses

Restricted Liu estimator under stochastic linear restrictions in generalized linear models: theory and applications

Penalized logistic regression with prior information for microarray gene expression classification

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