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AI Summary & Key Takeaways
AI Insight⚡ TL;DR(Too Long; Didn't Read)
Then these obtained biased estimators are compared with theunbiased estimator by the means of their mean square error.
📌 3 Key Takeaways
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Objective & Hypothesis:In this study, biased estimators for the shape parameter of a classicalPareto distribution are proposed using two dierent shrinkage tech-niques which give a smaller mean square error than an unbiased esti-mator.
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Key Findings & Data:In this study, biased estimators for the shape parameter of a classicalPareto distribution are proposed using two dierent shrinkage tech-niques which give a smaller mean square error than an unbiased esti-mator.
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Conclusion & Impact:Then these obtained biased estimators are compared with theunbiased estimator by the means of their mean square error.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:Mean Square Error · Pareto Distribution · Shape Parameter
Abstract
In this study, biased estimators for the shape parameter of a classicalPareto distribution are proposed using two dierent shrinkage tech-niques which give a smaller mean square error than an unbiased esti-mator. Then these obtained biased estimators are compared with theunbiased estimator by the means of their mean square error.
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Cite This Article
Özdemir, Ş., Ebegil, M. (2022). Two different shrinkage estimator classes for the shape parameter of classical Pareto distribution. Hacettepe Journal of Mathematics and Statistics, Cilt: 45 - Sayı: 4, 1231–1244. https://ajindex.com/articles/530159