SPLIDA

Software for Life Data Analysis. Click here for information on SPLIDA, a collection of S-Plus functions for Reliability Data Analysis. These functions were developed and used for the purpose of doing the examples in Meeker and Escobar. Included in the distribution are data sets and instructions on how to replicate almost all of the analyses in Meeker and Escobar. The current version of SPLIDA has an S-Plus graphical user interface (GUI) for much of its functionality. This version of SPLIDA will work S-Plus versions 6.x and 7.x. A command version of SPLIDA for R is under development.


References in zbMATH (referenced in 259 articles , 1 standard article )

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  1. Coolen, Frank P. A.; Ahmadini, Abdullah A. H.; Coolen-Maturi, Tahani: Imprecise inference based on the log-rank test for accelerated life testing (2021)
  2. Finkelstein, Maxim; Cha, Ji Hwan; Ghosh, Shyamal: Optimal inspection for missions with a possibility of abortion or switching to a lighter regime (2021)
  3. Morita, Lia H. M.; Tomazella, Vera L. D.; Ramos, Pedro L.; Ferreira, Paulo H.; Louzada, Francisco: The random deterioration rate model with measurement error based on the inverse Gaussian distribution (2021)
  4. Subramanian, Sundarraman: Median regression from twice censored data (2021)
  5. Zhang, Nan; Fouladirad, Mitra; Barros, Anne; Zhang, Jun: Reliability and maintenance analysis of a degradation-threshold-shock model for a system in a dynamic environment (2021)
  6. Zhao, Xiujie; Chen, Piao; Gaudoin, Olivier; Doyen, Laurent: Accelerated degradation tests with inspection effects (2021)
  7. Zheng, De-qiang; Fang, Xiang-zhong: Exact confidence limits for the parameter of an exponential distribution in the accelerated life tests under type-I censoring (2021)
  8. Dolgov, Sergey; Anaya-Izquierdo, Karim; Fox, Colin; Scheichl, Robert: Approximation and sampling of multivariate probability distributions in the tensor train decomposition (2020)
  9. He, Daojiang; Tao, Mingzhu: Statistical analysis for the doubly accelerated degradation Wiener model: an objective Bayesian approach (2020)
  10. Jiang, Peihua; Wang, Bing Xing; Wang, Xiaofei; Qin, Shuidan: Optimal plan for Wiener constant-stress accelerated degradation model (2020)
  11. Li, Jialu; Tian, Yubin; Wang, Dianpeng: Change-point detection of failure mechanism for electronic devices based on Arrhenius model (2020)
  12. Liu, Di; Wang, Shaoping; Tomovic, Mileta M.; Zhang, Chao: An evidence theory based model fusion method for degradation modeling and statistical analysis (2020)
  13. Mazucheli, Josmar; Bertoli, Wesley; Oliveira, Ricardo P.; Menezes, André F. B.: On the discrete quasi xgamma distribution (2020)
  14. Moghimbeygi, M.; Golalizadeh, M.: Spherical logistic distribution (2020)
  15. Salles, Gabriel; Mercier, Sophie; Bordes, Laurent: Semiparametric estimate of the efficiency of imperfect maintenance actions for a gamma deteriorating system (2020)
  16. Shan, Qianqian; Hong, Yili; Meeker, William Q.: Seasonal warranty prediction based on recurrent event data (2020)
  17. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  18. Xin, Hua; Zhu, Jian-Ping: Accelerated life testing for double-truncated general half normal distribution (2020)
  19. Xu, Ancha; Hu, Jiawen; Wang, Pingping: Degradation modeling with subpopulation heterogeneities based on the inverse Gaussian process (2020)
  20. Xu, Ancha; Wang, You-Gan; Zheng, Shurong; Cai, Fengjing: Bias reduction in the two-stage method for degradation data analysis (2020)

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