On the failure rate estimation of the inverse gaussian distribution

Abstract

New estimators of the inverse Gaussian failure rate are proposed based on the maximum likelihood predictive densities derived by Yang (1999). These estimators are compared, via Monte Carlo simulation, with the usual maximum likelihood estimators of the failure rate and found to be superior in terms of bias and mean squared error. Sensitivity of the estimators against the departure from the inverse Gaussian distribution is studied.

Keywords:

• Bias
• Failure rate
• Inverse Gaussian distribution
• Maximum likelihood predictive density
• Relative efficiency
• Sensitivity

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Notes

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