Prof. Dr. MAHMUDE REVAN &#214;ZKALE ATICIOĞLU

Hacettepe Journal of Mathematics and Statistics , cilt.54, sa.3, ss.1187-1205, 2025 (SCI-Expanded, Scopus, TRDizin)

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

Özkale M. R., Mammadova U.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.54, sa.5, ss.1400-1422, 2025 (SCI-Expanded)

3. Performance analysis of shrinkage estimators in Conway-Maxwell-Poisson regression model

Communications in Statistics - Theory and Methods , 2025 (SCI-Expanded, Scopus)

4. **Deviance residual-based Shewhart control chart for monitoring Conway-Maxwell-Poisson profile under the r-k class estimator**

Mammadova U., Ozkale M. R.

QUALITY TECHNOLOGY AND QUANTITATIVE MANAGEMENT , cilt.21, sa.6, ss.958-979, 2024 (SCI-Expanded, Scopus)

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

Genc M., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.53, sa.11, ss.5242-5264, 2024 (SCI-Expanded)

6. The ridge prediction error sum of squares statistic in linear mixed models

Kuran Ö., Özkale M. R.

METRIKA , cilt.87, sa.7, ss.757-773, 2024 (SCI-Expanded)

7. Lasso regression under stochastic restrictions in linear regression: An application to genomic data

Genc M., Özkale M. R.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.53, sa.8, ss.2816-2839, 2024 (SCI-Expanded)

8. Detecting shifts in Conway-Maxwell-Poisson profile with deviance residual-based CUSUM and EWMA charts under multicollinearity

STATISTICAL PAPERS , cilt.65, sa.2, ss.597-643, 2024 (SCI-Expanded)

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

Yıldırım H., Özkale M. R.

Cognitive Computation , cilt.16, sa.2, ss.641-653, 2024 (SCI-Expanded)

10. 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 , cilt.53, sa.5, ss.1419-1437, 2024 (SCI-Expanded)

11. Iterative Stochastic Restricted r-k Class Estimator in Generalized Linear Models: Application on Logistic Regression

Yildirim H., Ozkale M. R.

Iranian Journal of Science , 2024 (Scopus)

12. Bootstrap confidence interval of ridge regression in linear regression model: A comparative study via a simulation study

Özkale M. R., Altuner H.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.52, sa.20, ss.7405-7441, 2023 (SCI-Expanded)

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

SOFT COMPUTING , cilt.27, sa.5, ss.2493-2508, 2023 (SCI-Expanded)

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

Özkale M. R., Kuran O.

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE , cilt.34, sa.28, 2022 (SCI-Expanded)

15. Iterative restricted OK estimator in generalized linear models and the selection of tuning parameters via MSE and genetic algorithm

Özkale M. R., Abbasi A.

STATISTICAL PAPERS , cilt.63, sa.6, ss.1979-2040, 2022 (SCI-Expanded)

16. Conway-Maxwell Poisson regression-based control charts under iterative Liu estimator for monitoring count data

APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY , cilt.38, sa.4, ss.695-725, 2022 (SCI-Expanded)

17. The red indicator and corrected VIFs in generalized linear models

Yildirim H., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.50, sa.12, ss.4144-4170, 2021 (SCI-Expanded)

18. Stochastic restricted Liu predictors in linear mixed models

ÖZKALE M. R., Kuran O.

Communications In Statistics-Simulation And Computation , cilt.50, sa.9, ss.2561-2580, 2021 (SCI-Expanded)

19. LL-ELM: A regularized extreme learning machine based on L-1-norm and Liu estimator

NEURAL COMPUTING & APPLICATIONS , cilt.33, sa.16, ss.10469-10484, 2021 (SCI-Expanded)

20. The stochastic restricted ridge estimator in generalized linear models

Ozkale M. R., Nyquist H.

STATISTICAL PAPERS , cilt.62, sa.3, ss.1421-1460, 2021 (SCI-Expanded)

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

Özkale M. R., Altuner H.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , 2021 (SCI-Expanded)

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

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS , cilt.388, 2021 (SCI-Expanded)

23. Improvement of mixed predictors in linear mixed models

Kuran O., Özkale M. R.

JOURNAL OF APPLIED STATISTICS , cilt.48, sa.5, ss.924-942, 2021 (SCI-Expanded)

24. Usage of the GO estimator in high dimensional linear models

Genç M., Özkale M. R.

COMPUTATIONAL STATISTICS , cilt.36, ss.217-239, 2021 (SCI-Expanded)

25. Identification of outlying and influential data with principal components regression estimation in binary logistic regression

Abbasi A., ÖZKALE ATICIOĞLU M. R.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.50, sa.3, ss.609-630, 2021 (SCI-Expanded)

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

Kuran O., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.50, sa.2, ss.581-607, 2021 (SCI-Expanded)

27. 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 , cilt.50, sa.2, ss.594-611, 2021 (SCI-Expanded)

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

Ozkale M. R., Kuran O.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.49, sa.12, ss.3171-3195, 2020 (SCI-Expanded)

29. An Enhanced Extreme Learning Machine Based on Liu Regression

Yıldırım H., Özkale M. R.

NEURAL PROCESSING LETTERS , cilt.52, sa.1, ss.421-442, 2020 (SCI-Expanded)

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

Yildirim H., Ozkale M. R.

EXPERT SYSTEMS WITH APPLICATIONS , cilt.134, ss.225-233, 2019 (SCI-Expanded)

31. Adaptation of the jackknifed ridge methods to the linear mixed models

Ozkale M. R., Kuran O.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , cilt.89, sa.18, ss.3413-3452, 2019 (SCI-Expanded)

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

Acar T. S., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.49, sa.3, ss.771-792, 2019 (SCI-Expanded)

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

Ozkale M. R., Arıcan E.

COMPUTATIONAL STATISTICS , cilt.34, sa.2, ss.683-712, 2019 (SCI-Expanded)

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

KURTOĞLU F., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.48, sa.4, ss.1191-1218, 2019 (SCI-Expanded)

35. The r - d class estimator in generalized linear models: applications on gamma, Poisson and binomial distributed responses

KURTOĞLU F., Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , cilt.89, sa.4, ss.615-640, 2019 (SCI-Expanded)

36. Model selection via conditional conceptual predictive statistic under ridge regression in linear mixed models

Kuran O., Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , cilt.89, sa.1, ss.155-187, 2019 (SCI-Expanded)

37. Restricted Liu estimator in generalized linear models: Monte Carlo simulation studies on gamma and Poisson distributed responses

HACETTEPE JOURNAL OF MATHEMATICS AND STATISTICS , cilt.48, sa.4, ss.1250-1276, 2019 (SCI-Expanded)

38. Logistic regression diagnostics in ridge regression

Ozkale M. R., Lemeshow S., STURDIVANT R.

COMPUTATIONAL STATISTICS , cilt.33, sa.2, ss.563-593, 2018 (SCI-Expanded)

39. Principal components regression and r-k class predictions in linear mixed models

Ozkale M. R., Kuran O.

LINEAR ALGEBRA AND ITS APPLICATIONS , cilt.543, ss.173-204, 2018 (SCI-Expanded)

40. Liu estimator in partly linear regression models with correlated errors

Tabakan G., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.46, sa.3, ss.1958-1973, 2017 (SCI-Expanded)

41. An evaluation of ridge estimator in linear mixed models: an example from kidney failure data

Ozkale M. R., CAN F.

JOURNAL OF APPLIED STATISTICS , cilt.44, sa.12, ss.2251-2269, 2017 (SCI-Expanded)

42. Influence measures based on confidence ellipsoids in general linear regression model with correlated regressors

Acar T. S., Ozkale M. R.

JOURNAL OF APPLIED STATISTICS , cilt.43, sa.15, ss.2791-2812, 2016 (SCI-Expanded)

43. Liu estimation in generalized linear models: application on gamma distributed response variable

KURTOĞLU F., Ozkale M. R.

STATISTICAL PAPERS , cilt.57, sa.4, ss.911-928, 2016 (SCI-Expanded)

44. Iterative algorithms of biased estimation methods in binary logistic regression

SÖKÜT AÇAR T., Özkale M. R.

STATISTICAL PAPERS , cilt.57, sa.4, ss.991-1016, 2016 (SCI-Expanded)

45. Gilmour's approach to mixed and stochastic restricted ridge predictions in linear mixed models

Kuran O., Ozkale M. R.

LINEAR ALGEBRA AND ITS APPLICATIONS , cilt.508, ss.22-47, 2016 (SCI-Expanded)

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

Computational Statistics , cilt.31, sa.3, ss.879-898, 2016 (SCI-Expanded)

47. A new biased estimator in logistic regression model

Özkale M. R., Arıcan E.

Statistics , cilt.50, sa.2, ss.233-253, 2016 (SCI-Expanded)

48. Cross validation of ridge regression estimator in autocorrelated linear regression models

Acar T. S., Ozkale M. R.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , cilt.86, sa.12, ss.2429-2440, 2016 (SCI-Expanded)

49. First-order r - d class estimator in binary logistic regression model

Özkale M. R., Arican E.

STATISTICS & PROBABILITY LETTERS , cilt.106, ss.19-29, 2015 (SCI-Expanded)

50. Predictive performance of linear regression models

STATISTICAL PAPERS , cilt.56, sa.2, ss.531-567, 2015 (SCI-Expanded)

51. Leverages and Influential Observations in a Regression Model with Autocorrelated Errors

Ozkale M. R., Acar T. S.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.44, sa.11, ss.2267-2290, 2015 (SCI-Expanded)

52. The relative efficiency of the restricted estimators in linear regression models

JOURNAL OF APPLIED STATISTICS , cilt.41, sa.5, ss.998-1027, 2014 (SCI-Expanded)

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

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.43, sa.5, ss.1143-1186, 2014 (SCI-Expanded)

54. Influence measures in affine combination type regression

JOURNAL OF APPLIED STATISTICS , cilt.40, sa.10, ss.2219-2243, 2013 (SCI-Expanded)

55. Combining the unrestricted estimators into a single estimator and a simulation study on the unrestricted estimators

KAÇIRANLAR S., SAKALLIOĞLU S., Ozkale M. R., GÜLER H.

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , cilt.82, sa.5, ss.653-688, 2012 (SCI-Expanded)

56. More on the restricted ridge regression estimation

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION , cilt.81, sa.11, ss.1433-1448, 2011 (SCI-Expanded)

57. A stochastic restricted ridge regression estimator

JOURNAL OF MULTIVARIATE ANALYSIS , cilt.100, sa.8, ss.1706-1716, 2009 (SCI-Expanded)

58. Comment on Ridge Estimation to the Restricted Linear Model

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.38, sa.7, ss.1094-1097, 2009 (SCI-Expanded)

59. Principal components regression estimator and a test for the restrictions

Batah F. S. M., Ozkale M. R., Gore S. D.

STATISTICS , cilt.43, sa.6, ss.541-551, 2009 (SCI-Expanded)

60. Combining Unbiased Ridge and Principal Component Regression Estimators

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.38, sa.13, ss.2201-2209, 2009 (SCI-Expanded)

61. A jackknifed ridge estimator in the linear regression model with heteroscedastic or correlated errors

Oezkale M. R.

STATISTICS & PROBABILITY LETTERS , cilt.78, sa.18, ss.3159-3169, 2008 (SCI-Expanded)

62. Comparisons of the r-k class estimator to the ordinary least squares estimator under the Pitman's closeness criterion

Ozkale M. R., KAÇIRANLAR S.

STATISTICAL PAPERS , cilt.49, sa.3, ss.503-512, 2008 (SCI-Expanded)

63. Superiority of the r-d class estimator over some estimators by the mean square error matrix criterion

Ozkale M. R., Kaciranlar S.

STATISTICS & PROBABILITY LETTERS , cilt.77, sa.4, ss.438-446, 2007 (SCI-Expanded)

64. The restricted and unrestricted two-parameter estimators

Oezkale M. R., KAÇIRANLAR S.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.36, ss.2707-2725, 2007 (SCI-Expanded)

65. A prediction-oriented criterion for choosing the biasing parameter in Liu estimation

Oezkale M. R., Kaciranlar S.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.36, ss.1889-1903, 2007 (SCI-Expanded)

66. Comparisons of the unbiased ridge estimation to the other estimations

Ozkale M. R., Kaciranlar S.

COMMUNICATIONS IN STATISTICS-THEORY AND METHODS , cilt.36, ss.707-723, 2007 (SCI-Expanded)

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

Mammadova U., Ozkale M. R.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION , cilt.52, sa.3, 1 (SCI-Expanded)

Hakemli Bilimsel Toplantılarda Yayımlanmış Bildiriler 44

1. CUSUM and EWMA charts for ıdentifying anomalies in COM-Poisson profiles under multicollinearity

III. International Applied Statistics Conference UYIK-2022, Skopje, Makedonya, 22 - 24 Haziran 2022, ss.79, (Özet Bildiri)

2. Addressing multicollinearity via modified Shewhart chart in monitoring COM-Poisson profiles

Mammadova U., ÖZKALE M. R.

III. International Applied Statistics Conference UYIK-2022, Skopje, Makedonya, 22 - 24 Haziran 2022, ss.78, (Özet Bildiri)

3. Inference for various models to combat multicollinearity and their applications in various fields

Özkale M. R.

Örebro University School of Business, Seminars, Örebro, İsveç, 11 Kasım 2021, (Özet Bildiri)

4. Regularizing Least Squares and Extreme Learning Machine in High Dimensionality and Multicollinearity

Özkale M. R.

15th Iranian Statistics Conference, Yazd, İran, 9 - 11 Eylül 2020, ss.1, (Tam Metin Bildiri)

5. Ridge deviance residual charts for monitoring Poisson distributed data

5th International Researchers, Statisticians and Young Statisticians Congress,, İzmir, Türkiye, 18 - 20 Ekim 2019, ss.1, (Özet Bildiri)

6. The GO estimator: A new generalization of Lasso

GENÇ M., ÖZKALE M. R.

Young Business and Industrial Statisticians (y-BİS) Workshop on Recent Advances in Data Science and Business Analytics, İstanbul, Türkiye, 25 - 28 Eylül 2019, ss.2, (Özet Bildiri)

7. Stochastic linear restrictions in generalized linear models

ÖZKALE ATICIOĞLU M. R.

Young Business and Industrial Statisticians (y-BİS) Workshop on Recent Advances in Data Science and Business Analytics, İstanbul, Türkiye, 25 - 28 Eylül 2019, ss.80, (Özet Bildiri)

8. The Performance of ELM Based Ridge Regression via The Regularization Parameters

YILDIRIM H., ÖZKALE ATICIOĞLU M. R.

International Congress on Statistics, Mathematics and Analytical Methods, İstanbul, Türkiye, 28 Mart 2019, ss.308, (Özet Bildiri)

9. A New Extreme Learning Machine Algorithm Using Liu Regression

Yıldırım H., ÖZKALE M. R.

2nd International Congress on Statistics Mathmetics and Analytical Methods, İstanbul, Türkiye, 18 Mart 2019, ss.309, (Özet Bildiri)

10. A Comparative Study On The Effect Of Regularization Parameters On Extreme Learning Machine Based on Ridge Regression

Yıldırım H., ÖZKALE M. R.

2nd International Congress on Statistics Mathmetics and Analytical Methods, İstanbul, Türkiye, 18 Mart 2019, ss.308, (Özet Bildiri)

11. A new collinearity diagnostic in generalized linear models

ÖZKALE M. R.

Trends and Perspectives in Linear Statistical Inference (LinStat2018), Bedlewo, Polonya, 20 - 24 Ağustos 2018, ss.120, (Özet Bildiri)

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

ÖZKALE M. R., Söküt Açar T.

International Conference on Trends and Perspectives in Linear Statistical Inference (LINSTAT2018), Poznan, Polonya, 20 - 24 Ağustos 2018, ss.121-122, (Özet Bildiri)

13. Generalized linear model based control charts with Poisson response

Mamadova U., Özkale M. R.

4th International Researchers, Statisticians and Young Statisticians Congress, İzmir, Türkiye, 28 - 30 Nisan 2018, ss.1, (Özet Bildiri)

14. Influential observations on the confidence ellipsoids of generalized least squares and ridge regression estimators

Söküt Açar T., ÖZKALE M. R.

XVIII. International Symposium on Econometrics, Operations Research and Statistics, Trabzon, Türkiye, 5 - 07 Ekim 2017, ss.1, (Özet Bildiri)

15. Restricted alternatives to ridge and lasso regression: a random effects approach

GENÇ M., ÖZKALE M. R.

XVIII. International Symposium onEconometrics, Operations Research and Statistics, Trabzon, Türkiye, 5 - 07 Ekim 2017, ss.1, (Özet Bildiri)

16. Simulation on average run length comparison of multivariate control charts

Babaarslan K. D., ÖZKALE M. R.

XVIII. International Symposium on Econometrics, Operations Research and Statistics, Trabzon, Türkiye, 5 - 07 Ekim 2017, ss.1, (Özet Bildiri)

17. Adjustment of the jackknifed ridge method to the linear mixed models

KURTOĞLU F., ÖZKALE M. R.

3rd Researchers-Statisticians and Young Statisticians Congress, 24 - 26 Mayıs 2017, (Özet Bildiri)

18. On ridge parameter estimators for generalized linear mixed models: simulation study on Gamma response variable

3rd Researchers-Statisticians and Young Statisticians Congress (3rd IRSYSC-2017), Konya, Türkiye, 24 - 26 Mayıs 2017, ss.162, (Özet Bildiri)

19. Assessing the biased predictors in linear mixed models via a hypothetical data analysis

KURTOĞLU F., ÖZKALE M. R.

3rd Researchers-Statisticians and Young Statisticians Congress (3rd IRSYSC-2017), Konya, Türkiye, 24 - 26 Mayıs 2017, ss.66, (Özet Bildiri)

20. On estimating the regression coefficients in generalized linear models via a r-d class estimator

3rd Researchers-Statisticians and Young Statisticians Congress (3rd IRSYSC-2017), Konya, Türkiye, 24 - 26 Mayıs 2017, ss.121, (Özet Bildiri)

21. Determining the efficiency of classification algorithms on different data sets

YILDIRIM H., ÖZKALE M. R.

1st International Mediterranean Science and Engineering Congress (IMSEC2016), Adana, Türkiye, 26 - 28 Ekim 2016, ss.4348, (Özet Bildiri)

22. Linear mixed model selection under ridge regession based on conceptual predictive statistic

Söküt Açar T., ÖZKALE M. R.

International Conference on Trends and Perspectives in Linear Statistical Inference (LINSTAT-2016), İstanbul, Türkiye, 22 - 25 Ağustos 2016, ss.50, (Özet Bildiri)

23. Assessing The Influential Observations For Linear Regression Model With Second-order Autoregressive Errors

International Conference on Trends and Perspectives in Linear Statistical Inference (LINSTAT-2016), İstanbul, Türkiye, 22 - 25 Ağustos 2016, ss.19, (Özet Bildiri)

24. Linear mixed model selection under ridge regression based on the conceptual predictive statistic

KURAN Ö., ÖZKALE M. R.

Trends and Perspectives in Linear Statistical Inference (LinStat2016), İstanbul, Türkiye, 22 Ağustos - 25 Haziran 2016, (Özet Bildiri)

25. A first-order approximated restricted Liu estimation in generalized linear models

KURTOĞLU F., ÖZKALE M. R.

International Conference on Information Complexity and Statistical Modeling in High Dimensions with Applications (IC-SMHD-2016), Nevşehir, Türkiye, 18 - 21 Mayıs 2016, ss.57, (Özet Bildiri)

26. Estimation in linear mixed models under stochastic linear restrictions: mixed and stochastic restricted ridge estimations

International Conference on Information Complexity and Statistical Modeling in High Dimensions with Applications (IC-SMHD-2016), Nevşehir, Türkiye, 18 - 21 Mayıs 2016, ss.35, (Özet Bildiri)

27. Motivation of Liu Estimation Method to Linear Mixed Models