References
- Barandela, R., R. M. Valdovinos, J.S. Sánchez, J. S., and F. J. Ferri. 2004. The imbalanced training sample problem: under or over sampling? In Structural, Syntactic, and Statistical Pattern Recognition, ed. A. Fred, T.M. Caelli, R.P.W. Duin, A.C. Campilho, and D. de Ridder, 806–814. Berlin, Heidelberg: Springer.
- Birnbaum, Z. W. 1979. On the mathematics of competing risks, 79–1351. Hyattsville, MD: Education and Welfare Publication (PHS).
- Breiman, L. 2001. Random forests. Machine Learning 45 (1):5–32. doi: 10.1023/A:1010933404324.
- Breiman, L. J. Friedman, R. Olshen, and C. Stone. 1984. Classification and regression trees. New York, NY: Taylor & Francis.
- Crowder, M. J. 2012. Multivariate survival analysis and competing risks. New York, NY: Chapman & Hall.
- David, H. A. 1981. Order statistics. Hoboken, NJ: John Wiley & Sons Inc.
- David, H. A, and M. L. Moeschberger. 1978. Theory of competing risks. London: Griffin.
- Deng, H., G. Runger, and E. Tuv. 2012. System monitoring with real-time contrasts. Journal of Quality Technology 44 (1):9–27. doi: 10.1080/00224065.2012.11917878.
- Haghighi, F., and P. Castagliola. 2019. Control chart for monitoring the weibull shape parameter under two competing risks. Communications in Statistics – Simulation and Computation 48 (7):2125–37. doi: 10.1080/03610918.2018.1433845.
- James, G. D. Witten, T. Hastie, and R. Tibshirani. 2013. An introduction to statistical learning with applications in R. New York, NY: Springer.
- Kalbfleisch, J. D. and R.L. Prentice. 2002. The statistical analysis of failure time data. Hoboken, NJ: Wiley.
- Kubát, M. and S. Matwin. 1997. Addressing the curse of imbalanced training sets: one-sided selection. In Proceedings of the 14th International Conference on Machine Learning: 79–186.
- Meeker, W. Q. L. A. Escobar, and F. G. Pascual. 2021. Statistical methods for reliability data. Hoboken, NJ: John Wiley & Sons Inc.
- Montgomery, D. C. 2012. Introduction to statistical quality control. New York, NY: John Wiley & Sons Inc.
- Moustakides, G. V. 1986. Optimal stopping times for detecting changes in distributions. The Annals of Statistics 14 (4):1379–87. doi: 10.1214/aos/1176350164.
- Oshiro, T. M., P. S. Perez, and J. A. Baranauskas. 2012. How many trees in a random forest? In International workshop on machine learning and data mining in pattern recognition, 154–168. Berlin, Heidelberg: Springer.
- Page, E. S. 1954. Continuous inspection schemes. Biometrika 1 (2):100–15.
- Park, M. Y., T. Hastie, and R. Tibshirani. 2007. Averaged gene expressions for regression. Biostatistics (Oxford, England) 8 (2):212–27. doi: 10.1093/biostatistics/kxl002.
- Perlich, C., F. Provost, and J. S. Simonoff. 2003. Tree induction vs. logistic regression: a learning-curve analysis. Journal of Machine Learning Research 4:211–55.
- R Development Core Team. 2021. R project.
- Steiner, S. H., and R. J. MacKay. 2001. Monitoring processes with data censored owing to competing risks by using exponentially weighted moving average control charts. Applied Statistics 50 (3):292–302.
- Tolosi, L., and T. Lengauer. 2011. Classification with correlated features: unreliability of feature ranking and solutions. Bioinformatics 27 (14):1986–94.
- Wu, H., and W. Q. Meeker. 2002. Early detection of reliability problems using information from warranty databases. Technometrics 44 (2):120–33. doi: 10.1198/004017002317375073.