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Open AccessArticle

Estimating the Entropy for Lomax Distribution Based on Generalized Progressively Hybrid Censoring

Department of Mathematics, Beijing Jiaotong University, Beijing 100044, China
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Symmetry 2019, 11(10), 1219; https://doi.org/10.3390/sym11101219
Received: 22 August 2019 / Revised: 25 September 2019 / Accepted: 27 September 2019 / Published: 1 October 2019
As it is often unavoidable to obtain incomplete data in life testing and survival analysis, research on censoring data is becoming increasingly popular. In this paper, the problem of estimating the entropy of a two-parameter Lomax distribution based on generalized progressively hybrid censoring is considered. The maximum likelihood estimators of the unknown parameters are derived to estimate the entropy. Further, Bayesian estimates are computed under symmetric and asymmetric loss functions, including squared error, linex, and general entropy loss function. As we cannot obtain analytical Bayesian estimates directly, the Lindley method and the Tierney and Kadane method are applied. A simulation study is conducted and a real data set is analyzed for illustrative purposes.
Keywords: generalized progressively hybrid censoring; Lomax distribution; entropy; maximum likelihood estimator; Lindley method; Tierney and Kadane method; loss function generalized progressively hybrid censoring; Lomax distribution; entropy; maximum likelihood estimator; Lindley method; Tierney and Kadane method; loss function
MDPI and ACS Style

Liu, S.; Gui, W. Estimating the Entropy for Lomax Distribution Based on Generalized Progressively Hybrid Censoring. Symmetry 2019, 11, 1219.

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