Article Details

Abstract

Under the Hybrid Censoring sample data, this paper studies the statistical analysis problem of competing failure products for inverse Lomax distribution. Firstly, based on the competing failure products for inverse Lomax distribution, it utilizes maximum likelihood theory to obtain the Maximum Likelihood Estimation of unknown parameters, and uses asymptotic likelihood theory to derive the Approximate Confidence Interval of unknown parameters. Secondly, by setting the uninformative prior as the prior distribution of the unknown parameters, the Bayes estimation and HPD Confidence Interval of the parameters are obtained by using the MH sampling algorithm. Finally, through Monte Carlo simulation, the Mean Squared Error (MSE), Mean Absolute Bias (MAB), Average Interval Length (AL), and coverage percentage of the parameters are calculated, and the two estimation methods are compared. The experimental results show that Bayes Estimation is superior to Maximum Likelihood Estimation, and under the same confidence level, the average confidence interval length of HPD based on Bayes estimation is better than the approximate confidence interval average interval length of MLE.

Keywords

competing failure; inverse Lomax distribution; Maximum Likelihood Estimation; Bayes Estimation; MH sampling algorithm

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Funding

National Natural Science Foundation of China (11901134)

About the authors

Han Rong (1999-), female, Han ethnicity, from Bijie, Guizhou, pursuing a master's degree in reliability testing; Corresponding author: Cai Jing (1980-), female, Han ethnicity, from Weifang, Shandong Province, Professor, PhD, research direction: reliability testing, applied statistics; He Jian (1999-), male, born in Bijie, Guizhou, is pursuing a master's degree in reliability testing; He Fei (1998-), male, Han ethnicity, from Zunyi, Guizhou, is currently pursuing a master's degree in reliability testing.