Yazar "Ozkale, M. Revan" seçeneğine göre listele

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    Adaptation of the jackknifed ridge methods to the linear mixed models
    (Taylor & Francis Ltd, 2019) Ozkale, M. Revan; Kuran, Ozge
    The purpose of this article is to obtain the jackknifed ridge predictors in the linear mixed models and to examine the superiorities, the linear combinations of the jackknifed ridge predictors over the ridge, principal components regression, r-k class and Henderson's predictors in terms of bias, covariance matrix and mean square error criteria. Numerical analyses are considered to illustrate the findings and a simulation study is conducted to see the performance of the jackknifed ridge predictors.
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    A further prediction method in linear mixed models: Liu prediction
    (Taylor & Francis Inc, 2020) Ozkale, M. Revan; Kuran, Ozge
    We propose the Liu estimator and the Liu predictor via the penalized log-likelihood approach in linear mixed models when multicollinearity is present. The necessary and sufficient conditions for the superiority of the Liu predictor over the best linear unbiased predictor and the ridge predictor of linear combinations of fixed and random effects in the sense of matrix and scalar mean square errors are examined. Furthermore, the selection of the Liu biasing parameter is given and the findings are illustrated with both a real data set and a simulation study. The study show that the Liu estimator and predictor outperform the ridge estimator and predictor and the blue and blup in the sense of mean square error for large degree of correlation and the degree of supremacy of the Liu estimator and predictor over the ridge estimator and predictor and the blue and blup increase as the Liu biasing parameter decreases.
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    Marginal ridge conceptual predictive model selection criterion in linear mixed models
    (Taylor & Francis Inc, 2021) Kuran, Ozge; Ozkale, M. Revan
    In linear mixed model selection under ridge regression, we propose the model selection criteria based on conceptual predictive () statistic.The first proposed criterion is marginal ridge C-p () statistic based on the expected marginal Gauss discrepancy. An improvement of MRCp (IMRCp) statistic is then suggested and demonstrated, which is also an asymptotically unbiased estimator of the expected marginal Gauss discrepancy. Finally, a real data analysis and a Monte Carlo simulation study are given to examine the performance of the proposed criteria.