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 232 articles , 1 standard article )

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  1. Dolgov, Sergey; Anaya-Izquierdo, Karim; Fox, Colin; Scheichl, Robert: Approximation and sampling of multivariate probability distributions in the tensor train decomposition (2020)
  2. He, Daojiang; Tao, Mingzhu: Statistical analysis for the doubly accelerated degradation Wiener model: an objective Bayesian approach (2020)
  3. Jiang, Peihua; Wang, Bing Xing; Wang, Xiaofei; Qin, Shuidan: Optimal plan for Wiener constant-stress accelerated degradation model (2020)
  4. Li, Jialu; Tian, Yubin; Wang, Dianpeng: Change-point detection of failure mechanism for electronic devices based on Arrhenius model (2020)
  5. Moghimbeygi, M.; Golalizadeh, M.: Spherical logistic distribution (2020)
  6. Salles, Gabriel; Mercier, Sophie; Bordes, Laurent: Semiparametric estimate of the efficiency of imperfect maintenance actions for a gamma deteriorating system (2020)
  7. Thach, Tien T.; Bris, Radim; Volf, Petr; Coolen, Frank P. A.: Non-linear failure rate: a Bayes study using Hamiltonian Monte Carlo simulation (2020)
  8. Xu, Ancha; Wang, You-Gan; Zheng, Shurong; Cai, Fengjing: Bias reduction in the two-stage method for degradation data analysis (2020)
  9. Zhang, Fode; Shi, Yimin: Geometry on the statistical manifold induced by the degradation model with soft failure data (2020)
  10. Zhu, Tiefeng: Statistical inference of Weibull distribution based on generalized progressively hybrid censored data (2020)
  11. Al abbasi, Jamal N.; Khaleel, Mundher A.; Abdal-hammed, Moudher Kh.; Loh, Yue Fang; Ozel, Gamze: A new uniform distribution with bathtub-shaped failure rate with simulation and application (2019)
  12. Bagheri, S. F.; Asgharzadeh, A.; Basiri, E.; Fernández, A. J.: One-sample prediction regions for future record intervals (2019)
  13. Balakrishnan, Narayanaswamy; Qin, Chengwei: First passage time of a Lévy degradation model with random effects (2019)
  14. Bedbur, S.; Kamps, U.: Confidence regions in step-stress experiments with multiple samples under repeated type-II censoring (2019)
  15. Bobotas, Panayiotis; Kateri, Maria: Optimal designs for step-stress models under interval censoring (2019)
  16. Gottschalk, Hanno; Saadi, Mohamed: Shape gradients for the failure probability of a mechanic component under cyclic loading: a discrete adjoint approach (2019)
  17. Guan, Qiang; Tang, Yincai; Xu, Ancha: Reference Bayesian analysis of inverse Gaussian degradation process (2019)
  18. Jiang, Pei Hua; Wang, Bing Xing; Wu, Fang Tao: Inference for constant-stress accelerated degradation test based on Gamma process (2019)
  19. Kumar, Nirpeksh: Exact distributions of tests of outliers for exponential samples (2019)
  20. Lima, Maria C. S.; Cordeiro, Gauss M.; Ortega, Edwin M. M.; Nascimento, Abraão D. C.: A new extended normal regression model: simulations and applications (2019)

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