Abstract
This paper discusses entropy estimations for two-parameter inverse Weibull distributions under adaptive type-II progressive hybrid censoring schemes. Estimations of entropy derived by maximum likelihood estimation method and Bayes estimation method are both considered. Different Bayes estimators using squared loss function, Linex loss function, general entropy loss function, and balanced loss function are derived. Numerical results are derived by Lindley’s approximation method. Especially, the interval estimation of entropy is derived through maximum likelihood estimation method. To test the effectiveness of the estimations, simulation studies are conducted. These entropy estimation methods are illustrated and applied to analyze a real data set.
Highlights
In the life testing field, different data collection models are created to simulate various realistic situations
After setting threshold time T, appearances of adaptive type-II progressive hybrid censoring schemes under different cases depend on whether the m-th failure occurs beyond T or before T
We focus on the entropy estimation of inverse Weibull distribution when samples derived under adaptive type-II progressive hybrid censoring schemes
Summary
In the life testing field, different data collection models are created to simulate various realistic situations. After setting threshold time T, appearances of adaptive type-II progressive hybrid censoring schemes under different cases depend on whether the m-th failure occurs beyond T (case I) or before T (case II). The authors of [4] estimated parameters, reliability functions, and hazard functions under adaptive type-II progressive hybrid censoring schemes for exponentiated Weibull distribution. Reference [5] applied classical and Bayesian estimation procedures for estimations of parameters from Inverse Weibull distribution based on the progressive type-II censored data set. The authors of [17] contributed to the entropy estimations of Weibull distribution using generalized progressive hybrid censored samples. We focus on the entropy estimation of inverse Weibull distribution when samples derived under adaptive type-II progressive hybrid censoring schemes.
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