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 226 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. Salles, Gabriel; Mercier, Sophie; Bordes, Laurent: Semiparametric estimate of the efficiency of imperfect maintenance actions for a gamma deteriorating system (2020)
  4. Xu, Ancha; Wang, You-Gan; Zheng, Shurong; Cai, Fengjing: Bias reduction in the two-stage method for degradation data analysis (2020)
  5. Zhang, Fode; Shi, Yimin: Geometry on the statistical manifold induced by the degradation model with soft failure data (2020)
  6. Zhu, Tiefeng: Statistical inference of Weibull distribution based on generalized progressively hybrid censored data (2020)
  7. Bagheri, S. F.; Asgharzadeh, A.; Basiri, E.; Fernández, A. J.: One-sample prediction regions for future record intervals (2019)
  8. Balakrishnan, Narayanaswamy; Qin, Chengwei: First passage time of a Lévy degradation model with random effects (2019)
  9. Bedbur, S.; Kamps, U.: Confidence regions in step-stress experiments with multiple samples under repeated type-II censoring (2019)
  10. Bobotas, Panayiotis; Kateri, Maria: Optimal designs for step-stress models under interval censoring (2019)
  11. Gottschalk, Hanno; Saadi, Mohamed: Shape gradients for the failure probability of a mechanic component under cyclic loading: a discrete adjoint approach (2019)
  12. Guan, Qiang; Tang, Yincai; Xu, Ancha: Reference Bayesian analysis of inverse Gaussian degradation process (2019)
  13. Jiang, Pei Hua; Wang, Bing Xing; Wu, Fang Tao: Inference for constant-stress accelerated degradation test based on Gamma process (2019)
  14. Kumar, Nirpeksh: Exact distributions of tests of outliers for exponential samples (2019)
  15. 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)
  16. Roy, Soumya; Pradhan, Biswabrata: Bayesian (C)-optimal life testing plans under progressive type-I interval censoring scheme (2019)
  17. Zhang, Fode; Ng, Hon Keung Tony; Shi, Yimin; Wang, Ruibing: Amari-Chentsov structure on the statistical manifold of models for accelerated life tests (2019)
  18. Arif, Osama H.; Eidous, Omar: Fourth-order kernel method for simple linear degradation model (2018)
  19. Azizi, Fariba; Haghighi, Firoozeh: Joint modeling of linear degradation and failure time data with masked causes of failure under simple step-stress test (2018)
  20. Duan, Fengjun; Wang, Guanjun: Optimal step-stress accelerated degradation test plans for inverse Gaussian process based on proportional degradation rate model (2018)

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