# References

Agresti, Alan. 2013. Categorical Data Analysis. 3rd ed. Wiley. https://www.amazon.com/Categorical-Analysis-Wiley-Probability-Statistics-ebook-dp-B00CAYUFM2/dp/B00CAYUFM2/ref=mt_kindle?_encoding=UTF8&me=&qid=.
Fawcett, Tom. 2005. An Introduction to ROC Analysis. ELSEVIER. https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf.
Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2017. The Elements of Statistical Learning. 2nd ed. New York, NY: Springer. https://web.stanford.edu/~hastie/ElemStatLearn/.
James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2013. An Introduction to Statistical Learning: With Applications in r. 1st ed. New York, NY: Springer. http://faculty.marshall.usc.edu/gareth-james/ISL/book.html.
Kuhn, Max, and Kjell Johnson. 2016. Applied Predictive Modeling. 1st ed. New York, NY: Springer. http://appliedpredictivemodeling.com/.
Laerd. 2015. Statistical Tutorials and Software Guides. https://statistics.laerd.com/.
Molnar, Christoph. 2020. Interpretable Machine Learning. https://christophm.github.io/interpretable-ml-book/.
Moore, Dirk F. 2016. Applied Survival Analysis Using r. 1st ed. New York, NY: Springer. https://eohsi.rutgers.edu/eohsi-directory/name/dirk-moore/.
Therneau, Terry, and Elizabeth Atkinson. 2019. An Introduction to Recursive Partitioning Using the RPART Routines. Boca Raton, Florida: Chapman; Hall/CRC. https://cran.r-project.org/web/packages/rpart/vignettes/longintro.pdf.

1. The related probit regression link function is $$f(E(Y|X)) = \Phi^{-1}(E(Y|X)) = \Phi^{-1}(\pi)$$. The difference between the logistic and probit link function is theoretical, and the practical significance is slight. You can safely ignore probit.↩︎

2. Notes from Machine Learning Mastery↩︎

3. See neat discussion in Note section of Surv() help file.↩︎

4. This formulation is derived from the relationship between the survival function to a baseline survival, $$S(t) = S_0(t)^\exp{Xb}$$. See German Rodriguez’s course notes.↩︎

5. Full discussion on Rens van de Schoot.↩︎