M. Revan Özkale

1.0k total citations
61 papers, 719 citations indexed

About

M. Revan Özkale is a scholar working on Statistics and Probability, Statistics, Probability and Uncertainty and Analytical Chemistry. According to data from OpenAlex, M. Revan Özkale has authored 61 papers receiving a total of 719 indexed citations (citations by other indexed papers that have themselves been cited), including 56 papers in Statistics and Probability, 23 papers in Statistics, Probability and Uncertainty and 12 papers in Analytical Chemistry. Recurrent topics in M. Revan Özkale's work include Advanced Statistical Methods and Models (54 papers), Statistical Methods and Inference (31 papers) and Advanced Statistical Process Monitoring (23 papers). M. Revan Özkale is often cited by papers focused on Advanced Statistical Methods and Models (54 papers), Statistical Methods and Inference (31 papers) and Advanced Statistical Process Monitoring (23 papers). M. Revan Özkale collaborates with scholars based in Türkiye, Pakistan and Sweden. M. Revan Özkale's co-authors include Selahattin Kaçıranlar, Sharad D. Gore, Rodney X. Sturdivant, Sadullah Sakallıoğlu, Stanley Lemeshow and Hans Nyquist and has published in prestigious journals such as Expert Systems with Applications, Neural Computing and Applications and Journal of Computational and Applied Mathematics.

In The Last Decade

M. Revan Özkale

56 papers receiving 702 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
M. Revan Özkale Türkiye 14 581 239 124 85 75 61 719
Mahdi Roozbeh Iran 14 558 1.0× 187 0.8× 117 0.9× 52 0.6× 56 0.7× 53 692
Selahattin Kaçıranlar Türkiye 15 769 1.3× 317 1.3× 164 1.3× 84 1.0× 18 0.2× 52 812
Gentiane Haesbroeck Belgium 11 397 0.7× 239 1.0× 29 0.2× 43 0.5× 93 1.2× 26 576
Sadullah Sakallıoğlu Türkiye 8 230 0.4× 88 0.4× 61 0.5× 42 0.5× 98 1.3× 9 381
Bo‐Cheng Wei China 16 583 1.0× 78 0.3× 28 0.2× 19 0.2× 139 1.9× 56 726
Gaorong Li China 20 1.0k 1.8× 82 0.3× 23 0.2× 160 1.9× 236 3.1× 84 1.2k
Song-Gui Wang China 11 214 0.4× 59 0.2× 18 0.1× 20 0.2× 35 0.5× 47 355
Dan J. Spitzner United States 7 241 0.4× 358 1.5× 27 0.2× 133 1.6× 50 0.7× 17 529
Dongdong Xiang China 13 230 0.4× 338 1.4× 28 0.2× 143 1.7× 44 0.6× 41 433
Irving W. Burr United States 15 354 0.6× 388 1.6× 17 0.1× 54 0.6× 57 0.8× 25 733

Countries citing papers authored by M. Revan Özkale

Since Specialization
Citations

This map shows the geographic impact of M. Revan Özkale's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by M. Revan Özkale with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites M. Revan Özkale more than expected).

Fields of papers citing papers by M. Revan Özkale

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by M. Revan Özkale. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by M. Revan Özkale. The network helps show where M. Revan Özkale may publish in the future.

Co-authorship network of co-authors of M. Revan Özkale

This figure shows the co-authorship network connecting the top 25 collaborators of M. Revan Özkale. A scholar is included among the top collaborators of M. Revan Özkale based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with M. Revan Özkale. M. Revan Özkale is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Özkale, M. Revan, et al.. (2025). Performance analysis of shrinkage estimators in Conway-Maxwell-Poisson regression model. Communication in Statistics- Theory and Methods. 55(1). 63–91.
2.
Özkale, M. Revan, et al.. (2024). Iterative stochastic restricted $r-d$ class estimator in generalized linear models: application to binomial, Poisson and negative binomial distributions. Hacettepe Journal of Mathematics and Statistics. 53(5). 1419–1437.
3.
Özkale, M. Revan, et al.. (2024). Iterative Stochastic Restricted $$r-k$$ Class Estimator in Generalized Linear Models: Application on Logistic Regression. Iranian Journal of Science. 49(2). 357–367.
4.
Özkale, M. Revan, et al.. (2023). Deviance residual-based Shewhart control chart for monitoring Conway-Maxwell-Poisson profile under the r-k class estimator. Quality Technology & Quantitative Management. 1–22. 2 indexed citations
5.
Özkale, M. Revan, et al.. (2021). The r-k class estimator in generalized linear models applicable with simulation and empirical study using a Poisson and Gamma responses. Hacettepe Journal of Mathematics and Statistics. 50(2). 594–611. 7 indexed citations
6.
Özkale, M. Revan, et al.. (2020). Usage of the GO estimator in high dimensional linear models. Computational Statistics. 36(1). 217–239. 6 indexed citations
7.
Özkale, M. Revan, et al.. (2019). Marginal ridge conceptual predictive model selection criterion in linear mixed models. Communications in Statistics - Simulation and Computation. 50(2). 581–607.
8.
Özkale, M. Revan, et al.. (2019). Adaptation of the jackknifed ridge methods to the linear mixed models. Journal of Statistical Computation and Simulation. 89(18). 3413–3452. 1 indexed citations
9.
Özkale, M. Revan & Hans Nyquist. (2019). The stochastic restricted ridge estimator in generalized linear models. Statistical Papers. 62(3). 1421–1460. 6 indexed citations
10.
Özkale, M. Revan, et al.. (2018). A further prediction method in linear mixed models: Liu prediction. Communications in Statistics - Simulation and Computation. 49(12). 3171–3195. 5 indexed citations
11.
Özkale, M. Revan, et al.. (2018). Model selection via conditional conceptual predictive statistic under ridge regression in linear mixed models. Journal of Statistical Computation and Simulation. 89(1). 155–187. 2 indexed citations
12.
Özkale, M. Revan, et al.. (2018). Restricted Liu estimator in generalized linear models: Monte Carlo simulation studies on gamma and Poisson distributed responses. Hacettepe Journal of Mathematics and Statistics. 48(3). 2 indexed citations
13.
Özkale, M. Revan, et al.. (2016). Gilmour's approach to mixed and stochastic restricted ridge predictions in linear mixed models. Linear Algebra and its Applications. 508. 22–47. 7 indexed citations
14.
Özkale, M. Revan, et al.. (2015). Influence measures in ridge regression when the error terms follow an Ar(1) process. Computational Statistics. 31(3). 879–898. 6 indexed citations
15.
Özkale, M. Revan. (2013). Influence measures in affine combination type regression. Journal of Applied Statistics. 40(10). 2219–2243. 12 indexed citations
16.
Özkale, M. Revan. (2011). Combining the unrestricted estimators into a single estimator and a simulation study on the unrestricted estimators. Journal of Statistical Computation and Simulation. 82(5). 653–688. 9 indexed citations
17.
Özkale, M. Revan, et al.. (2009). Combining Unbiased Ridge and Principal Component Regression Estimators. Communication in Statistics- Theory and Methods. 38(13). 2201–2209. 20 indexed citations
18.
Özkale, M. Revan. (2009). A stochastic restricted ridge regression estimator. Journal of Multivariate Analysis. 100(8). 1706–1716. 35 indexed citations
19.
Özkale, M. Revan & Selahattin Kaçıranlar. (2007). The Restricted and Unrestricted Two-Parameter Estimators. Communication in Statistics- Theory and Methods. 36(15). 2707–2725. 164 indexed citations
20.
Özkale, M. Revan & Selahattin Kaçıranlar. (2007). Comparisons of the Unbiased Ridge Estimation to the Other Estimations. Communication in Statistics- Theory and Methods. 36(4). 707–723. 7 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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