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Estimation of the scale parameter of the Lomax distribution under progressive censoring

A. Asgharzadeh, R. Valiollahi


For the Lomax distribution, the maximum likelihood method does not provide an explicit estimator for the scale parameter based on a progressively Type-II censored sample. In this paper, we first present a simple method of deriving an explicit estimator by approximating the likelihood function. We then examine through simulations the bias and the estimated risk (ER) of this estimator and show that this estimator is as efficient as the maximum likelihood estimator (MLE). An approximation based on Lindley’s method is used to obtain Bayes estimators. Next, we obtain an exact confidence interval for the scale parameter. Finally, we present a numerical example and a Mont Carlo simulation to illustrate all the methods of inference discussed here.


Maximum likelihood estimation, Bayes estimation, Fisher information, Monte Carlo simulation, LINEX and quadratic loss functions. Lomax distribution, Progressive Type-II censoring.

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