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Confidence Intervals with Jointly Type-II Censored Samples from Two Exponential Distributions

Kishan G. Mehrotra, G. K. Bhattacharyya

Open publisher page 21 citations

Abstract

Strong consistency and asymptotic normality of the maximum likelihood estimators are established in the context of jointly type-II censored samples from two exponential populations. Large-sample confidence intervals are derived for the individual scale parameters as well as their ratio, and some applications to series and parallel systems are discussed. For the ratio of the scale parameters, an exact confidence procedure is developed on the basis of the failure counts in the two samples, and its asymptotic efficiency is investigated.

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What this paper is about

Strong consistency and asymptotic normality of the maximum likelihood estimators are established in the context of jointly type-II censored samples from two exponential populations. Large-sample confidence intervals are derived for the individual scale parameters as well as their ratio, and some applications to series and parallel systems are discussed. For the ratio of the scale parameters, an exact confidence procedure is developed on the basis of the failure counts in the two samples, and its asymptotic efficiency is investigated.

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Available abstract

Strong consistency and asymptotic normality of the maximum likelihood estimators are established in the context of jointly type-II censored samples from two exponential populations. Large-sample confidence intervals are derived for the individual scale parameters as well as their ratio, and some applications to series and parallel systems are discussed. For the ratio of the scale parameters, an exact confidence procedure is developed on the basis of the failure counts in the two samples, and its asymptotic efficiency is investigated.

Key concepts: Mathematics, Confidence interval, Statistics, Estimator, Consistency (knowledge bases), Asymptotic distribution, Exponential function, Exponential distribution

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