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Estimation of the smallest scale parameter of two-parameter exponential distributions

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  • Panayiotis Bobotas

Abstract

Improved point and interval estimation of the smallest scale parameter of n independent populations following two-parameter exponential distributions are studied. The model is formulated in such a way that allows for treating the estimation of the smallest scale parameter as a problem of estimating an unrestricted scale parameter in the presence of a nuisance parameter. The classes of improved point and interval estimators are enriched with Stein-type, Brewster and Zidek-type, Maruyama-type and Strawderman-type improved estimators under both quadratic and entropy losses, whereas using as a criterion the coverage probability, with Stein-type, Brewster and Zidek-type, and Maruyama-type improved intervals. The sampling framework considered incorporates important life-testing schemes such as i.i.d. sampling, type-II censoring, progressive type-II censoring, adaptive progressive type-II censoring, and record values.

Suggested Citation

  • Panayiotis Bobotas, 2019. "Estimation of the smallest scale parameter of two-parameter exponential distributions," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 48(11), pages 2748-2765, June.
    • Handle: RePEc:taf:lstaxx:v:48:y:2019:i:11:p:2748-2765
    DOI: 10.1080/03610926.2018.1472792
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