References
- Abernethy, R. B. (2006). The new Weibull handbook: Reliability and statistical analysis for predicting life, safety, supportability, risk, cost and warranty claims (5th ed.). New York, NY: Barringer and Associates.
- Badar, M. G., & Priest, A. M. (1982). Statistical aspects of fiber and bundle strength in hybrid composites. In T. Hayashi, K. Kawata, & S. Umekawa (Eds.), Progress in science and engineering composites (pp. 1129–1136). Tokyo.
- Balakrishnan, N., & Kateri, M. (2008). On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data. Statistics and Probability Letters, 78, 2971–2975.
- Fleming, T. R. (2008). Current issues in non-inferiority trials. Statistics in Medicine, 27, 317–332.
- Hayter, A. J. (2012). Win-probabilities for regression models. Statistical Methodology, 9, 520–527.
- Hayter, A. J. (2013). Inferences on the difference between future observations for comparing two treatments. Journal of Applied Statistics, 40, 887–900.
- Hayter, A. J. (2014). Identifying common normal distributions. Test, 23, 135–152.
- Hayter, A. J. (in press). Win-probabilities for comparing two Poisson variables. Communications in Statistics -- Theory and Methods.
- Hayter, A. J., & Kiatsupaibul, S. (2013). Exact inferences for a Weibull model. Quality Engineering, 25, 175–180.
- Hudak, D., & Tiryakioglu, M. (2011). On comparing the shape parameters of two Weibull distributions. Materials Science and Engineering A, 528, 8028–8030.
- Kundu, D., & Gupta, R. D. (2006). Estimation of {\it P}[{\it Y}≤ {\it X} ] for Weibull distributions. IEEE Transactions on Reliability, 55, 270–280.
- Kwong, K. S., Cheung, S. H., Hayter, A. J., & Wen, M. (2012). Extension of three-arm non-inferiority studies to trials with multiple new treatments. Statistics in Medicine, 31, 2833–2843.
- Lawless, J. F. (2003). Statistical models and methods for lifetime data analysis (2nd ed.). New York, NY: John Wiley.
- Lin, C. T., & Ke, S. J. (2013). Estimation of {\it P}({\it Y}≤ {\it X}) for location-scale distributions under joint progressively Type-II right censoring. Quality Technology & Quantitative Management, 10, 339–352.
- Louzada-Neto, F., Bolfarine, H., & Rodrigues, J. (2002). Comparing two Weibull models with accelerated data. Statistics, 36, 175–184.
- Marsaglia, G., Tsang, W. W., & Wang, J. (2003). Evaluating Kolmogorov’s distribution. Journal of Statistical Software, 8, 18.
- Murthy, D. N. P., Bulmer, M., & Eccleston, J. A. (2004a). Weibull model selection for reliability modelling. Reliability Engineering and System Safety, 86, 257–267.
- Murthy, D. N. P., Xie, M. & Jiang, R. (2004b). Weibull models. New York, NY: John Wiley.
- Nadarajah, S., & Kotz, S. (2008). Strength modeling using Weibull distributions. Journal of Mechanical Science and Technology, 22, 1247–1254.
- Parsi, S., Ganjali, M., & Farsipour, N. S. (2011). Conditional maximum likelihood and interval estimation for two Weibull populations under joint Type-II progressive censoring. Communications in Statistics -- Theory and Methods, 40, 2117–2135.
- Rinne, H. (2008). The Weibull distribution: A handbook. New York: NY, CRC Press.
- Schafer, R. E., & Sheffield, T. S. (1976). On procedures for comparing two Weibull populations. Technometrics, 18, 231–235.
- Simard, R., & L’Ecuyer, P. (2011). Computing the two-sided Kolmogorov-Smirnov distribution. Journal of Statistical Software, 39, 11.
- Wiwatwattana, N., Hayter, A. J. & Kiatsupaibul, S. (2015). Win-probabilities for comparing two binary outcomes. Communications in Statistics - Simulation and Computation, in press.