Journal of Guangxi Normal University(Natural Science Edition) ›› 2024, Vol. 42 ›› Issue (3): 159-169.doi: 10.16088/j.issn.1001-6600.2023062001
Previous Articles Next Articles
ZHU Yan, CAI Jing*, LONG Fang
[1] 鄢伟安, 杨海军, 周俊杰. 基于自适应逐步Ⅱ型混合截尾试验Burr-Ⅻ分布的统计分析[J].系统工程理论与实践, 2020, 40(5): 1339-1349. [2] 林慧中, 王亮. 考虑交互效应的双应力恒加试验可靠性推断[J]. 昆明理工大学学报(自然科学版), 2023, 48(1): 180-192. [3] NASSAR M, ELSHAHHAT A. Statistical analysis of inverse Weibull constant-stress partially accelerated life tests with adaptive progressively type I censored data[J]. Mathematics, 2023, 11(2): 370. [4] 师义民, 师小琳. 竞争失效产品部分加速寿命试验的统计分析[J]. 西北工业大学学报, 2017, 35(1): 109-115. [5] AKGUL F G, YU K, SENOGLU B. Classical and Bayesian inferences in step-stress partially accelerated life tests for inverse Weibull distribution under type-I censoring[J]. Strength of Materials, 2020, 52(3): 480-496. [6] EL-SAGHEER R M, MAHMOUD M A W, NAGATY H. Inferences for Weibull-exponential distribution based on progressive type-II censoring under step-stress partially accelerated life test model[J]. Journal of Statistical Theory and Practice, 2018, 13(1): 14. [7] MOHAMEDM A W, EL-SAGHEER R M., ABOU SENNA A M. Estimating the modified Weibull parameters in presence of step-stress partially accelerated life testing[J]. Journal of Statistics Applications & Probability, 2018, 7(1): 137-150. [8] ALJOHANIH M, ALFAR N M. Estimations with step-stress partially accelerated life tests for competing risks Burr XII lifetime model under type-II censored data[J]. Alexandria Engineering Journal, 2020, 59(3): 1171-1180. [9] REN J R, GUI W H. Inference and optimal censoring scheme for progressively type-II censored competing risks model for generalized Rayleigh distribution[J]. Computational Statistics, 2021, 36: 479-513. [10] ZHANG C F, SHI Y M, WU M. Bayesian inference on type-I progressively hybrid competing risks model[J]. Chinese Quarterly Journal of Mathematics, 2018, 33(2): 122-131. [11] WU M, SHI Y M, SUN Y D. Inference for accelerated competing failure models from Weibull distribution under type-I progressive hybrid censoring[J]. Journal of Computational and Applied Mathematics, 2014, 263: 423-431. [12] ABU-ZINADAH H H, SAYED-AHMED N. Competing risks model with partially step-stress accelerate life tests in analyses lifetime Chen data under type-II censoring scheme[J]. Open Physics, 2019, 17(1): 192-199. [13] 王琪, 兰海英. 复合Rayleigh分布模型尺度参数的Bayes估计[J]. 科学技术与工程, 2012, 12(30): 7980-7982. [14] ABUSHAL T A. Estimation of the unknown parameters for the compound Rayleigh distribution based on progressive first-failure-censored sampling[J]. Open Journal of Statistics, 2011, 1(3): 161-171. [15] 邵媛媛, 周菊玲, 董翠玲. 双边定时截尾样本下复合瑞利分布的参数估计[J].淮阴师范学院学报(自然科学版), 2019, 18(2): 95-100. [16] BAROT D R, PATEL M N. Posterior analysis of the compound Rayleigh distribution under balanced loss functions for censored data[J]. Communications in Statistics-Theory and Methods, 2017, 46(3): 1317-1336. [17] 王琪, 兰海英, 徐刚. 复合瑞利分布模型参数的Bayes可靠性分析[J]. 江西师范大学学报(自然科学版), 2013, 37(1): 20-22. [18] GHITANY M E. A compound Rayleigh survival model and its application to randomly censored data[J]. Statistical Papers, 2001, 42(4): 437-450. [19] BEKKER A, ROUX J J J, MOSTEIT P J. A generalization of the compound Rayleigh distribution: using a Bayesian method on cancer survival times[J]. Communications in Statistics-Theory and Methods, 2000, 29(7): 1419-1433. [20] BHATTACHARYYA G K, SOEJOETI Z. A tampered failure rate model for step-stress accelerated life test[J]. Communications in Statistics-Theory and Methods, 1989, 18(5): 1627-1643. [21] EL-SAGHEER R M, AHSANULLAH M. Bayesianestimation based on progressively type-II censored samples from compound Rayleigh distribution[J]. Journal of Statistical Theory and Applications, 2015, 14(2):107-122. [22] GEWEKE J, TANIZAKI H. Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling[J]. Computational Statistics and Data Analysis, 2001, 37(2): 151-170. |
[1] | ZHAO Xiaomei, DING Yong, WANG Haitao. Maximum Likelihood DOA Estimation Based on Improved Monarch Butterfly Algorithm [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(3): 131-140. |
[2] | LONG Fang, CAI Jing, ZHU Yan. Analysis of Reliability in a Multicomponent Stress-Strength Model for Lomax Distribution under Progressive type-Ⅱ Hybrid Censoring [J]. Journal of Guangxi Normal University(Natural Science Edition), 2024, 42(2): 120-130. |
[3] | SUN Ye, JIANG Jingjing, WANG Chunjie. Bayesian Estimation of Current Status Data with Generalized Extreme Value Regression Model [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(1): 82-90. |
[4] | LIANG Xin, CHEN Xiaoling, ZHANG Xingfa, LI Yuan. A Class of Autoregressive Moving Average Model with GARCH Type Errors [J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(1): 195-205. |
[5] | LI Lili, ZHANG Xingfa, LI Yuan, DENG Chunliang. Daily GARCH Model Estimation Using High Frequency Data [J]. Journal of Guangxi Normal University(Natural Science Edition), 2021, 39(4): 68-78. |
[6] | YANG Xiaowei, ZHANG Junjian. Law of Iterated Logarithm and Strong Consistency for Negative Binomial Regression Model [J]. Journal of Guangxi Normal University(Natural Science Edition), 2020, 38(3): 59-69. |
[7] | DING Xin-yue, XU Mei-ping. Bayesian Estimation for Scale Parameter of Inverse Gamma Distribution under Mlinex Loss Function [J]. Journal of Guangxi Normal University(Natural Science Edition), 2014, 32(3): 61-64. |
|