M. Revan Özkale

24 papers Journal 24
YearRankTypeTitle / Venue / Authors
2025 J jnl
Restricted Liu estimator under stochastic linear restrictions in generalized linear models: theory and applications.
Commun. Stat. Simul. Comput.
Atif Abbasi, M. Revan Özkale
2024 J jnl
A Novel Regularized Extreme Learning Machine Based on L
Cogn. Comput.
Hasan Yildirim, M. Revan Özkale
2024 J jnl
Regularization and variable selection with triple shrinkage in linear regression: a generalization of lasso.
Commun. Stat. Simul. Comput.
Murat Genç, M. Revan Özkale
2023 J jnl
A combination of ridge and Liu regressions for extreme learning machine.
Soft Comput.
Hasan Yildirim, M. Revan Özkale
2023 J jnl
Bootstrap selection of ridge regularization parameter: a comparative study via a simulation study.
Commun. Stat. Simul. Comput.
M. Revan Özkale, Hüsniye Altuner
2023 J jnl
Comparison of deviance and ridge deviance residual-based control charts for monitoring Poisson profiles.
Commun. Stat. Simul. Comput.
Ulduz Mammadova, M. Revan Özkale
2022 J jnl
Model selection via conditional conceptual predictive statistic for mixed and stochastic restricted ridge estimators in linear mixed models.
Concurr. Comput. Pract. Exp.
M. Revan Özkale, Özge Kuran
2021 J jnl
LL-ELM: A regularized extreme learning machine based on L
Neural Comput. Appl.
Hasan Yildirim, M. Revan Özkale
2021 J jnl
Marginal ridge conceptual predictive model selection criterion in linear mixed models.
Commun. Stat. Simul. Comput.
Özge Kuran, M. Revan Özkale
2021 J jnl
Profile monitoring for count data using Poisson and Conway-Maxwell-Poisson​ regression-based control charts under multicollinearity problem.
J. Comput. Appl. Math.
Ulduz Mammadova, M. Revan Özkale
2021 J jnl
Stochastic restricted Liu predictors in linear mixed models.
Commun. Stat. Simul. Comput.
Özge Kuran, M. Revan Özkale
2021 J jnl
The red indicator and corrected VIFs in generalized linear models.
Commun. Stat. Simul. Comput.
M. Revan Özkale
2021 J jnl
Usage of the GO estimator in high dimensional linear models.
Comput. Stat.
Murat Genç, M. Revan Özkale
2020 J jnl
A further prediction method in linear mixed models: Liu prediction.
Commun. Stat. Simul. Comput.
M. Revan Özkale, Özge Kuran
2020 J jnl
An Enhanced Extreme Learning Machine Based on Liu Regression.
Neural Process. Lett.
Hasan Yildirim, M. Revan Özkale
2020 J jnl
Regression diagnostics methods for Liu estimator under the general linear regression model.
Commun. Stat. Simul. Comput.
Tugba Söküt Açar, M. Revan Özkale
2019 J jnl
A first-order approximated jackknifed ridge estimator in binary logistic regression.
Comput. Stat.
M. Revan Özkale, Engin Arican
2019 J jnl
Restricted ridge estimator in generalized linear models: Monte Carlo simulation studies on Poisson and binomial distributed responses.
Commun. Stat. Simul. Comput.
Fikriye Kurtoglu, M. Revan Özkale
2019 J jnl
The performance of ELM based ridge regression via the regularization parameters.
Expert Syst. Appl.
Hasan Yildirim, M. Revan Özkale
2018 J jnl
Logistic regression diagnostics in ridge regression.
Comput. Stat.
M. Revan Özkale, Stanley Lemeshow, Rodney X. Sturdivant
2017 J jnl
Liu estimator in partly linear regression models with correlated errors.
Commun. Stat. Simul. Comput.
Gülin Tabakan, M. Revan Özkale
2016 J jnl
Influence measures in ridge regression when the error terms follow an Ar(1) process.
Comput. Stat.
Tugba Söküt Açar, M. Revan Özkale
2014 J jnl
Monte Carlo Simulation Study of Biased Estimators in the Linear Regression Models with Correlated or Heteroscedastic Errors.
Commun. Stat. Simul. Comput.
M. Revan Özkale
2009 J jnl
A stochastic restricted ridge regression estimator.
J. Multivar. Anal.
M. Revan Özkale