Accelerated Event Times with Multiple Thresholds

References

• Berger, J. O., Bernardo, J. M., and Sun, D. (2009), “The Formal Definition of Reference Priors,” The Annals of Statistics, 37, 905–938. DOI: 10.1214/07-AOS587.
   Web of Science ®Google Scholar
• Collins, D. H., Weaver, B. P., and Hamada, M. S. (2017), “Panning for Gold: Enhancing the Precision of Sensitivity Test Data,” Statistical Analysis and Data Mining, 10, 166–181. DOI: 10.1002/sam.11345.
   Web of Science ®Google Scholar
• Crowder, M. J. (2001), Classical Competing Risks, Boca Raton, FL: CRC Press.
   Google Scholar
• Dixon, W. J. (1965), “The Up-and-Down Method for Small Samples,” Journal of the American Statistical Association, 60, 967–978. DOI: 10.1080/01621459.1965.10480843.
   Web of Science ®Google Scholar
• Dixon, W. J., and Mood, A. M. (1948)., “A Method for Obtaining and Analyzing Sensitivity Data,” Journal of the American Statistical Association, 43, 109–126. DOI: 10.1080/01621459.1948.10483254.
   Web of Science ®Google Scholar
• Dror, H. A., and Steinberg, D. M. (2008), “Sequential Experimental Designs for Generalized Linear Models,” Journal of the American Statistical Association, 103, 288–298. DOI: 10.1198/016214507000001346.
   Web of Science ®Google Scholar
• Dunn, P. K., Smyth, G. K. (1996), “Randomized Quantile Residuals,” Journal of Computational and Graphical Statistics, 5, 236–244. DOI: 10.2307/1390802.
   Google Scholar
• Efromovich, S., and Thomas, E. V. (1996), “Application of Nonparametric Binary Regression to Evaluate the Sensitivity of Explosives,” Technometrics, 38, 50–58. DOI: 10.1080/00401706.1996.10484415.
   Web of Science ®Google Scholar
• Farewell, V. T., and Prentice, R. L. (1977), “A Study of Distributional Shape in Life Testing,” Technometrics, 19, 69–75. DOI: 10.2307/1268257.
   Web of Science ®Google Scholar
• Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019), “Visualization in Bayesian Workflow,” Journal of the Royal Statistical Society, Series A, 182, 389–402. DOI: 10.1111/rssa.12378.
   Web of Science ®Google Scholar
• Gabry, J., and Mahr, T. (2022), “bayesplot: Plotting for Bayesian Models,” R package version 1.9.0, available at https://mc-stan.org/bayesplot/.
   Google Scholar
• Mallory, H. D. (1956), “The Development of Impact Sensitivity Tests at the Explosives Research Laboratory, Bruceton, Pennsylvania during the years 1941-1945,” Technical Report, Naval Ordnance Lab, White Oak, MD.
   Google Scholar
• Maxim, L. D., Hendrickson, A. D., and Cullen, D. E. (1977), “Experimental Design for Sensitivity Testing: The Weibull Model,” Technometrics, 19, 405–412. DOI: 10.1080/00401706.1977.10489579.
   Web of Science ®Google Scholar
• Meeker, W. Q. (1987), “Limited Failure Population Life Tests: Application to Integrated Circuit Reliability,” Technometrics, 29, 51–65. DOI: 10.1080/00401706.1987.10488183.
   Web of Science ®Google Scholar
• Meeker, W. Q., Hahn, G. J., and Escobar, L. A. (2017), Statistical Intervals: A Guide for Practitioners and Researchers (Vol. 541), Hoboken, NJ: Wiley.
   Google Scholar
• Meeker, W. Q., Escobar, L. A., and Pascual, F. G. (2022), Statistical Methods for Reliability Data (2nd ed.), Hoboken, NJ: Wiley.
   Google Scholar
• Neyer, B. T. (1994), “A d-optimality-based Sensitivity Test,” Technometrics, 36, 61–70. DOI: 10.2307/1269199.
   Web of Science ®Google Scholar
• Odunayo, B. J. (2018), “On a Truncated Accelerated Plan for Two Component Parallel Systems under Ramp-Stress Testing Using Masked Data for Weibull Distribution,” Covenant Journal of Physical and Life Sciences, 6, 49–57.
   Google Scholar
• R Core Team. (2022), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/.
   Google Scholar
• Robbins, H., and Monro, S. (1951), “A Stochastic Approximation Method,” The Annals of Mathematical Statistics, 22, 400–407. DOI: 10.1214/aoms/1177729586.
   Google Scholar
• Ross, G. J. S. (2012). Nonlinear Estimation, New York: Springer.
   Google Scholar
• Shi, X., Liu, Y., and Shi, Y. (2017), “Statistical Analysis for Masked Hybrid System Lifetime Data in Step-Stress Partially Accelerated Life Test with Progressive Hybrid Censoring,” PloS One, 12, 1–15. DOI: 10.1371/journal.pone.0186417.
   Web of Science ®Google Scholar
• Sitter, R. R., and Wu, C. F. J. (1993), “Optimal Designs for Binary Response Experiments: Fieller, D, and A Criteria,” Scandinavian Journal of Statistics, 20, 329–341.
   Web of Science ®Google Scholar
• Srivastava, P. W., and Savita. (2016), “Design of Ramp-Stress Accelerated Life Test Plans for a Parallel System with Two Dependent Components,” International Journal of Performability Engineering, 12, 241–248.
   Google Scholar
• Srivastava, P. W., and Savita. (2017), “Design of Ramp-Stress Accelerated Life Test Plans for a Parallel System with Two Independent Components Using Masked Data,” International Journal of Reliability and Applications, 18, 45–63.
   Google Scholar
• Vehtari, A., Gabry, J., Magnusson, M., Yao, Y., Bürkner, P.-C., Paananen, T., and Gelman, A. (2022), “loo: Efficient Leave-one-out Cross-Validation and Waic for Bayesian Models,” R package version 2.5.1 available at https://mc-stan.org/loo/.
   Google Scholar
• Wang, D., Tian, Y., and Wu, C. F. J. (2015), “A Skewed Version of the Robbins-Monro-Joseph Procedure for Binary Response,” Statistica Sinica, 25, 1679–1689. DOI: 10.5705/ss.2014.197.
   Web of Science ®Google Scholar
• Wang, D., Tian, Y., and Wu, C. F. J. (2020), “Comprehensive Comparisons of Major Sequential Design Procedures for Sensitivity Testing,” Journal of Quality Technology, 52, 155–167.
   Web of Science ®Google Scholar
• Wu, C. F. J. (1985), “Efficient Sequential Designs with Binary Data,” Journal of the American Statistical Association, 80, 974–984. DOI: 10.1080/01621459.1985.10478213.
   Web of Science ®Google Scholar
• Wu, C. F. J., and Tian, Y. (2014), “Three-Phase Optimal Design of Sensitivity Experiments,” Journal of Statistical Planning and Inference, 149, 1–15. DOI: 10.1016/j.jspi.2013.10.007.
   Web of Science ®Google Scholar
• Young, L. J., and Easterling, R. G. (1994), “Estimation of Extreme Quantiles based on Sensitivity Tests: A Comparative Study,” Technometrics, 36, 48–60. DOI: 10.1080/00401706.1994.10485400.
   Web of Science ®Google Scholar