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2003•Unpublished venueRequires access

A New Way to Estimate the Confidence Interval for the Mean of the Exponential Distribution Based on Grouped Data

Fei He-liang

Open publisher page 1 citations

Abstract

Based on grouped data, the asymptotic nonnality of the maximum likelihood estimate (MLE) for mean of the single-parameter exponential distribution is proved from a new point of view, and the asymptotic confidence interval is derived. Comparing the results of CHEN and MIE, a Monte carlo simulation shows that it is a little more effective.

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What this paper is about

Based on grouped data, the asymptotic nonnality of the maximum likelihood estimate (MLE) for mean of the single-parameter exponential distribution is proved from a new point of view, and the asymptotic confidence interval is derived. Comparing the results of CHEN and MIE, a Monte carlo simulation shows that it is a little more effective.

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Available abstract

Based on grouped data, the asymptotic nonnality of the maximum likelihood estimate (MLE) for mean of the single-parameter exponential distribution is proved from a new point of view, and the asymptotic confidence interval is derived. Comparing the results of CHEN and MIE, a Monte carlo simulation shows that it is a little more effective.

Key concepts: Confidence interval, Mathematics, Statistics, Exponential function, Exponential distribution, Monte Carlo method, Point estimation, Grouped data

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