Abstract

This paper provides an entropy inference method based on an objective Bayesian approach for upper record values having the two-parameter logistic distribution. We derive the entropy based on i-th upper record value and the joint entropy based on the upper record values, and examine their properties. For objective Bayesian analysis, we provide objective priors such as the Jeffreys and reference priors for unknown parameters of the logistic distribution based on upper record values. Then, an entropy inference method based on the objective priors is developed. In real data analysis, we assess the quality of the proposed models under the objective priors.

Highlights

  • Shannon [1] proposed information theory to quantify information loss and introduces statistical entropy

  • Kang et al [4] derived estimators of the entropy of a double-exponential distribution based on multiply Type-II censored samples by using maximum likelihood estimators (MLEs) and approximate MLEs (AMLEs)

  • This paper provides an entropy inference method based on an objective Bayesian approach for upper record values having the two-parameter logistic distribution

Read more

Summary

Introduction

Shannon [1] proposed information theory to quantify information loss and introduces statistical entropy. Baratpour et al [2] provided the entropy of a continuous probability distribution with upper record values and several bounds for this entropy by using the hazard rate function. Abo-Eleneen [3] suggested an efficient computation method for entropy in progressively Type-II censored samples. Kang et al [4] derived estimators of the entropy of a double-exponential distribution based on multiply Type-II censored samples by using maximum likelihood estimators (MLEs) and approximate MLEs (AMLEs). Seo and Kang [5] developed estimation methods for entropy by using estimators of the shape parameter in the generalized half-logistic distribution based on Type-II censored samples. This paper provides an entropy inference method based on an objective Bayesian approach for upper record values having the two-parameter logistic distribution. The 3rd International Electronic and Flipped Conference on Entropy and Applications (ECEA 2016), 1–10 November 2016; Sciforum Electronic Conference Series, Vol 3, 2016

Objective Priors
Entropy
Application
Findings
Conclusions

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.