M. Revan Özkale

According to our database, M. Revan Özkale authored at least 24 papers between 2009 and 2025.

Collaborative distances:

• Dijkstra number of six.
• Erdős number of five.

2025

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

Atif Abbasi, M. Revan Özkale

Commun. Stat. Simul. Comput., May, 2025

2024

Regularization and variable selection with triple shrinkage in linear regression: a generalization of lasso.

Murat Genç, M. Revan Özkale

Commun. Stat. Simul. Comput., November, 2024

A Novel Regularized Extreme Learning Machine Based on L₁-Norm and L₂-Norm: a Sparsity Solution Alternative to Lasso and Elastic Net.

Hasan Yildirim, M. Revan Özkale

Cogn. Comput., March, 2024

2023

Bootstrap selection of ridge regularization parameter: a comparative study via a simulation study.

M. Revan Özkale, Hüsniye Altuner

Commun. Stat. Simul. Comput., August, 2023

A combination of ridge and Liu regressions for extreme learning machine.

Hasan Yildirim, M. Revan Özkale

Soft Comput., March, 2023

Comparison of deviance and ridge deviance residual-based control charts for monitoring Poisson profiles.

Ulduz Mammadova, M. Revan Özkale

Commun. Stat. Simul. Comput., March, 2023

2022

Model selection via conditional conceptual predictive statistic for mixed and stochastic restricted ridge estimators in linear mixed models.

M. Revan Özkale, Özge Kuran

Concurr. Comput. Pract. Exp., 2022

2021

LL-ELM: A regularized extreme learning machine based on L₁-norm and Liu estimator.

Hasan Yildirim, M. Revan Özkale

Neural Comput. Appl., 2021

Profile monitoring for count data using Poisson and Conway-Maxwell-Poisson​ regression-based control charts under multicollinearity problem.

Ulduz Mammadova, M. Revan Özkale

J. Comput. Appl. Math., 2021

Usage of the GO estimator in high dimensional linear models.

Murat Genç, M. Revan Özkale

Comput. Stat., 2021

The red indicator and corrected VIFs in generalized linear models.

M. Revan Özkale

Commun. Stat. Simul. Comput., 2021

Stochastic restricted Liu predictors in linear mixed models.

Özge Kuran, M. Revan Özkale

Commun. Stat. Simul. Comput., 2021

Marginal ridge conceptual predictive model selection criterion in linear mixed models.

Özge Kuran, M. Revan Özkale

Commun. Stat. Simul. Comput., 2021

2020

An Enhanced Extreme Learning Machine Based on Liu Regression.

Hasan Yildirim, M. Revan Özkale

Neural Process. Lett., 2020

A further prediction method in linear mixed models: Liu prediction.

M. Revan Özkale, Özge Kuran

Commun. Stat. Simul. Comput., 2020

Regression diagnostics methods for Liu estimator under the general linear regression model.

Tugba Söküt Açar, M. Revan Özkale

Commun. Stat. Simul. Comput., 2020

2019

The performance of ELM based ridge regression via the regularization parameters.

Hasan Yildirim, M. Revan Özkale

Expert Syst. Appl., 2019

A first-order approximated jackknifed ridge estimator in binary logistic regression.

M. Revan Özkale, Engin Arican

Comput. Stat., 2019

Restricted ridge estimator in generalized linear models: Monte Carlo simulation studies on Poisson and binomial distributed responses.

Fikriye Kurtoglu, M. Revan Özkale

Commun. Stat. Simul. Comput., 2019

2018

Logistic regression diagnostics in ridge regression.

M. Revan Özkale, Stanley Lemeshow, Rodney X. Sturdivant

Comput. Stat., 2018

2017

Liu estimator in partly linear regression models with correlated errors.

Gülin Tabakan, M. Revan Özkale

Commun. Stat. Simul. Comput., 2017

2016

Influence measures in ridge regression when the error terms follow an Ar(1) process.

Tugba Söküt Açar, M. Revan Özkale

Comput. Stat., 2016

2014

Monte Carlo Simulation Study of Biased Estimators in the Linear Regression Models with Correlated or Heteroscedastic Errors.

M. Revan Özkale

Commun. Stat. Simul. Comput., 2014

2009

A stochastic restricted ridge regression estimator.

M. Revan Özkale

J. Multivar. Anal., 2009