Robust estimators for one-shot device testing data under gamma lifetime model with an application to a tumor toxicological data

N. Balakrishnan, E. Castilla (), N. Martín and L. Pardo
Additional contact information
N. Balakrishnan: McMaster University
E. Castilla: Complutense University of Madrid
N. Martín: Complutense University of Madrid
L. Pardo: Complutense University of Madrid

Metrika: International Journal for Theoretical and Applied Statistics, 2019, vol. 82, issue 8, No 5, 1019 pages

Abstract: Abstract Due to its flexibility, gamma distribution is commonly used for lifetime data analysis in reliability and survival studies, and especially in one-shot device testing data. In the study of such data, inducing more failures by accelerated life tests is a common practice, to obtain more lifetime information within a relatively short period of time. In this paper, we develop weighted minimum density power divergence estimators, as a natural extension of the classical maximum likelihood estimator, in the analysis of one-shot device testing data, under accelerated life tests based on gamma lifetime distribution. Wald-type test statistics, based on these estimators, are also developed. Through a Monte Carlo simulation study, the suggested estimators and tests are shown to be robust alternatives to the maximum likelihood estimators and the classical Wald tests based on them. Finally, these procedures are applied to a mice tumor toxicological data for illustrative purpose.

Keywords: Gamma distribution; Maximum likelihood estimator; Minimum density power divergence estimator; Multiple stresses; One-shot devices; Robustness; Tumor toxicological data; Wald-type tests (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s00184-019-00718-5

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