Abstract
In this paper, we consider Dagum distribution which is capable of modeling various shapes of failure rates and aging criteria. Based on progressively type-I interval censoring data, we first obtain the maximum likelihood estimators and the approximate confidence intervals of the unknown parameters of the Dagum distribution. Next, we obtain the Bayes estimators of the parameters of Dagum distribution under the squared error loss (SEL) and balanced squared error loss (BSEL) functions using independent informative gamma and non informative uniform priors for both scale and two shape parameters. A Monte Carlo simulation study is performed to assess the performance of the proposed Bayes estimators with the maximum likelihood estimators. We also compute credible intervals and symmetric 100(1 − τ)% two-sided Bayes probability intervals under the respective approaches. Besides, based on observed samples, Bayes predictive estimates and intervals are obtained using one-and two-sample schemes. Simulation results reveal that the Bayes estimates based on SEL and BSEL performs better than maximum likelihood estimates in terms of bias and MSEs. Besides, credible intervals have smaller interval lengths than confidence interval. Further, predictive estimates based on SEL with informative prior performs better than non-informative prior for both one and two sample schemes. Further, the optimal censoring scheme has been suggested using a optimality criteria. Finally, we analyze a data set to illustrate the results derived.
Highlights
Introduction[1] introduced a heavy tail distribution, called the Dagum distribution, especially for modeling income distributions which could be used in place of log-normal and Pareto models
The results show that the performance of squared error loss (SEL) based on informative prior is more or less same as non-informative prior both in terms of bias and mean square error (MSE) values
In this paper we discussed parameter estimation for the Dagum distribution based on progressive type-I interval censored data
Summary
[1] introduced a heavy tail distribution, called the Dagum distribution, especially for modeling income distributions which could be used in place of log-normal and Pareto models. Several authors studied progressive type-I interval censored sampling schemes under varied conditions. In this regard, readers may refer to the works of [11,12,13,14,15,16,17,18,19,20,21,22,23]. The second objective is to obtain Bayes estimators under SEL and BSEL functions using independent gamma priors for both scale and shape parameters of the model. Bayesian estimation for Dagum distribution progressively type-I interval censored schemes perhaps, due to complexities in computational work.
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