Meta-analysis,pretest, and shrinkage estimation of kurtosis parameters |
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Authors: | Nighat Zahra Supranee Lisawadi S. Ejaz Ahmed |
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Affiliation: | 1. Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University, Thailand;2. Department of Mathematics and Statistics, Brock University, St. Catharines, ON, Canada |
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Abstract: | An asymptotic theory for the improved estimation of kurtosis parameter vector is developed for multi-sample case using uncertain prior information (UPI) that several kurtosis parameters are the same. Meta-analysis is performed to obtain pooled estimator, as it is a statistical methodology for pooling quantitative evidence. Pooled estimator is a good choice when assumption of homogeneity holds but it becomes inconsistent as assumption violates, therefore pretest and Stein-type shrinkage estimators are proposed as they combine sample and nonsample information in a superior way. Asymptotic properties of suggested estimators are discussed and their risk comparisons are also mentioned. |
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Keywords: | Asymptotic distributional bias Asymptotic quadratic risk Kurtosis Meta-Analysis Shrinkage and pretest estimation Simulation Uncertain prior information |
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