A family of shrinkage estimators for Weibull shape parameter in censored sampling

Autor:	Sharad Saxena, Harshada Joshi, Housila P. Singh
Rok vydání:	2006
Předmět:	Statistics and Probability Shrinkage estimator Minimum-variance unbiased estimator Efficient estimator Bias of an estimator Mean squared error Statistics Consistent estimator Estimator Statistics Probability and Uncertainty Invariant estimator Mathematics
Zdroj:	Statistical Papers. 49:513-529
ISSN:	1613-9798 0932-5026
Popis:	In this paper a new class of shrinkage estimators has been introduced for the shape parameter in an independently identically distributed two-parameterWeibull model under censored sampling. The main idea is to incorporate the prior guessed value by correcting the standard estimator, which is essentially an unbiased estimator, with optimally weighted ratios of the guessed value and the standard estimator, instead of considering a convex combination of the standard estimator and the difference of the guessed value and the standard estimator. The resulting estimator dominates the standard estimator in a surprisingly large neighborhood of the guessed value. The suggested estimator has also been compared with the minimum mean squared error estimator and a class of estimators suggested by Singh and Shukla in IAPQR Trans 25(2), 107–118, 2000. It is found that the suggested class of estimators has lesser bias as well as lesser mean squared error than its competitors subject to certain conditions.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a5c679e749f7ffdf29bd4000e8b39c11 https://doi.org/10.1007/s00362-006-0030-7 Zobrazit plný text záznamu Full text from SpringerLink