1988IEEE Transactions on ReliabilityRequires access

Robust estimators of the 2-parameter gamma distribution

Adil Adatia

Open publisher page 3 citations

Abstract

The available estimators for parameters of the gamma distribution are moment estimators, maximum-likelihood estimators, and approximations to the maximum-likelihood estimators. These estimators are not suitable for small samples; however, they are still being used at the present time. The proposed robust estimators for scale and shape parameters are more suitable for small samples. They have RMS (root-mean-square) errors that are considerably smaller than those of the other estimators. In addition, they are easier to calculate, and are therefore appropriate in many applications.>

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What this paper is about

The available estimators for parameters of the gamma distribution are moment estimators, maximum-likelihood estimators, and approximations to the maximum-likelihood estimators. These estimators are not suitable for small samples; however, they are still being used at the present time. The proposed robust estimators for scale and shape parameters are more suitable for small samples. They have RMS (root-mean-square) errors that are considerably smaller than those of the other estimators. In addition, they are easier to calculate, and are therefore appropriate in many applications.>

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OpenAlex reports 3 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

The available estimators for parameters of the gamma distribution are moment estimators, maximum-likelihood estimators, and approximations to the maximum-likelihood estimators. These estimators are not suitable for small samples; however, they are still being used at the present time. The proposed robust estimators for scale and shape parameters are more suitable for small samples. They have RMS (root-mean-square) errors that are considerably smaller than those of the other estimators. In addition, they are easier to calculate, and are therefore appropriate in many applications.>

Key concepts: Estimator, M-estimator, Extremum estimator, Mathematics, Gamma distribution, Statistics, Maximum likelihood, Distribution (mathematics)

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