Statistical Inference for Two Lomax Populations Under Balanced Joint Progressive Type-II Censoring Scheme

Yuanqi Wang, Jinchen Xiang and Wenhao Gui ()
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Yuanqi Wang: School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China
Jinchen Xiang: School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China
Wenhao Gui: School of Mathematics and Statistics, Beijing Jiaotong University, Beijing 100044, China

Mathematics, 2025, vol. 13, issue 9, 1-25

Abstract: In recent years, joint censoring schemes have gained significant attention in lifetime experiments and reliability analysis. A refined approach, known as the balanced joint progressive censoring scheme, has been introduced in statistical studies. This research focuses on statistical inference for two Lomax populations under this censoring framework. Maximum likelihood estimation is employed to derive parameter estimates, and asymptotic confidence intervals are constructed using the observed Fisher information matrix. From a Bayesian standpoint, posterior estimates of the unknown parameters are obtained under informative prior assumptions. To evaluate the effectiveness and precision of these estimators, a numerical study is conducted. Additionally, a real dataset is analyzed to demonstrate the practical application of these estimation methods.

Keywords: balanced joint progressive censoring; Lomax distribution; maximum likelihood estimation; Bayesian estimation; Metropolis–Hastings algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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