Inference for a general family of exponentiated distributions under ranked set sampling with partially observed complementary competing risks data
學年 112
學期 1
出版（發表）日期 2023-12-03
作品名稱 Inference for a general family of exponentiated distributions under ranked set sampling with partially observed complementary competing risks data
作品名稱（其他語言）
著者 Liang Wang; Yuhlong Lio; Yogesh Mani Tripathi; Tzong-Ru Tsai
單位
出版者
著錄名稱、卷期、頁數 Quality Technology & Quantitative Management. Accepted 03 Dec. 2023
摘要 Ranked set sampling (RSS) acts as an efficient way for collecting failure information due to its ability of saving testing time and cost, and this paper discusses statistical inference for complementary competing risks model under a modified RSS scheme called the maximum ranked set sampling procedure with unequal samples (MRSSU). When the lifetimes of causes of failure are characterized by a general family of exponentiated distributions with partially observed failure causes, parameter estimation is explored from classical likelihood and Bayesian approaches. Existence and uniqueness of maximum likelihood estimators for model parameters are established, and approximate confidence intervals are constructed in consequence. With respect to general flexible priors, Bayes point and interval estimates are constructed, and associated Monte-Carlo sampling is proposed for complex posterior computation. In addition, when there is extra restriction information available, likelihood and Bayes estimates are also proposed in this regard. Extensive simulation studies are conducted to investigate the performance of different methods, and a real-life example is carried out to demonstrate the applications of our results.
關鍵字 Ranked set sampling; complementary competing risks model; general family of exponentiated distributions; maximum likelihood estimation; Bayesian estimateon; order restriction
語言 en
ISSN 1684-3703
期刊性質 國外
收錄於 SCI Scopus
產學合作
通訊作者 Liang Wang
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/125555 )

SDGS 優質教育,夥伴關係