Bayesian and Frequentist Estimation of Stress-Strength Reliability from a New Extended Burr XII Distribution
Bayesian inference; Burr distribution; data analysis; maximum likelihood method; stress-strength reliability
In this article, we propose and study a new three-parameter heavy-tailed distribution that uni-fies the Burr type XII and power inverted Topp-Leone distributions in an original manner. This unification is made through the use of a simple shift parameter. Among its interesting function-alities, it exhibits possibly decreasing and unimodal probability density and hazard rate functions. We examine its quantile function, stochastic dominance, ordinary moments, weighted moments, incomplete moments, and stress-strength reliability coefficient. Then, the classical and Bayesian approaches are developed to estimate the model and stress-strength reliability parameters. Bayes estimates are obtained under the squared error and entropy loss functions. Simulated data are considered to point out the performance of the derived estimates based on the mean squared error. In the final part, the potential of the new model is exemplified by the analysis of two engineering data sets, showing that it is preferable to other reputable and comparable models. 2025, National Statistical Institute. All rights reserved.
Agiwal, Varun; Tyagi, Shikhar; Chesneau, Christophe
REVSTAT-Statistical Journal;Volume;23;Issue;1;pp.117-138
Restricted Access; Hardcopy may be available in the library
https://doi.org/10.57805/revstat.v23i1.544 https://www.scopus.com/pages/publications/85218745284?origin=resultslist
Agiwal V., Indian Institute of Public Health, Telangana, Hyderabad, India; Tyagi S., Department of Statistics and Data Science, Christ Deemed to be University, Bangalore, India; Chesneau C., Department of Mathematics, LMNO, Universitde Caen-Normandie, Caen, France