A stochastic restricted ridge regression estimator. - M. Revan Özkale (J. Multivar. Anal., 2009)

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)

Influence measures in ridge regression when the error terms follow an Ar(1) process. - Tugba Söküt Açar; M. Revan Özkale (Comput. Stat., 2016)

Liu estimator in partly linear regression models with correlated errors. - Gülin Tabakan; M. Revan Özkale (Commun. Stat. Simul. Comput., 2017)

Logistic regression diagnostics in ridge regression. - M. Revan Özkale; Stanley Lemeshow; Rodney X. Sturdivant (Comput. Stat., 2018)

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)

A first-order approximated jackknifed ridge estimator in binary logistic regression. - M. Revan Özkale; Engin Arican (Comput. Stat., 2019)

The performance of ELM based ridge regression via the regularization parameters. - Hasan Yildirim; M. Revan Özkale (Expert Syst. Appl., 2019)

Regression diagnostics methods for Liu estimator under the general linear regression model. - Tugba Söküt Açar; M. Revan Özkale (Commun. Stat. Simul. Comput., 2020)

A further prediction method in linear mixed models: Liu prediction. - M. Revan Özkale; Özge Kuran (Commun. Stat. Simul. Comput., 2020)

An Enhanced Extreme Learning Machine Based on Liu Regression. - Hasan Yildirim; M. Revan Özkale (Neural Process. Lett., 2020)

Marginal ridge conceptual predictive model selection criterion in linear mixed models. - Özge Kuran; 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)

The red indicator and corrected VIFs in generalized linear models. - M. Revan Özkale (Commun. Stat. Simul. Comput., 2021)

Usage of the GO estimator in high dimensional linear models. - Murat Genç; M. Revan Özkale (Comput. Stat., 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)

LL-ELM: A regularized extreme learning machine based on L1-norm and Liu estimator. - Hasan Yildirim; M. Revan Özkale (Neural Comput. Appl., 2021)

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)

Comparison of deviance and ridge deviance residual-based control charts for monitoring Poisson profiles. - Ulduz Mammadova; M. Revan Özkale (Commun. Stat. Simul. Comput., 2023)

A combination of ridge and Liu regressions for extreme learning machine. - Hasan Yildirim; M. Revan Özkale (Soft Comput., 2023)

Bootstrap selection of ridge regularization parameter: a comparative study via a simulation study. - M. Revan Özkale; Hüsniye Altuner (Commun. Stat. Simul. Comput., 2023)

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., 2024)

Regularization and variable selection with triple shrinkage in linear regression: a generalization of lasso. - Murat Genç; M. Revan Özkale (Commun. Stat. Simul. Comput., 2024)

Restricted Liu estimator under stochastic linear restrictions in generalized linear models: theory and applications. - Atif Abbasi; M. Revan Özkale (Commun. Stat. Simul. Comput., 2025)