Numerical computations indicate that certain of these estimators substantially improve the usual and minimum mean squared error (MMSE) estimators for value of the parameter near the prior estimate, especially for small sample sizes
📌 3 Key Takeaways
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Objective & Hypothesis:This article proposes a class of shrinkage estimators of Morgenstern type bivariate exponential distribution (MTBED) based on concomitants of order statistic in ranked set sampling (RSS).
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Key Findings & Data:The class of estimators for the parameter is motivated by the work of Jani (1991).
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Conclusion & Impact:Numerical computations indicate that certain of these estimators substantially improve the usual and minimum mean squared error (MMSE) estimators for value of the parameter near the prior estimate, especially for small sample sizes
🔬Design:Research Article
📍Scope:2008 Verileri
⚙️Variables:Ranked set sampling · Morgenstern type bivariate exponential distribution · Concomitants of order statistic
Abstract
This article proposes a class of shrinkage estimators of Morgenstern type bivariate exponential distribution (MTBED) based on concomitants of order statistic in ranked set sampling (RSS). The class of estimators for the parameter is motivated by the work of Jani (1991). The proposed class of shrinkage estimators has smaller mean square error (MSE) than the Chacko and Thomas (2008) estimators and minimum mean squared error (MMSE) estimators for wider range of the parameter. Numerical computations indicate that certain of these estimators substantially improve the usual and minimum mean squared error (MMSE) estimators for value of the parameter near the prior estimate, especially for small sample sizes
Mehta, V., Singh, H. P. (2022). Some classes of shrinkage estimators in the morgenstern type bivariate exponential distribution using ranked set sampling. Hacettepe Journal of Mathematics and Statistics, Cilt: 45 - Sayı: 2, 575–591. https://ajindex.com/articles/530077
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