References

Agrawal, Rakesh, Tomasz Imieliński, and Arun Swami. 1993. “Mining Association Rules Between Sets of Items in Large Databases.” In Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data, 207–16. SIGMOD ’93. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/170035.170072.

Bahdanau, Dzmitry, Kyunghyun Cho, and Yoshua Bengio. 2014. “Neural Machine Translation by Jointly Learning to Align and Translate.” CoRR abs/1409.0473. https://api.semanticscholar.org/CorpusID:11212020.

Belenky, Gregory, Nancy J. Wesensten, David R. Thorne, Maria L. Thomas, Helen C. Sing, Daniel P. Redmond, Michael B. Russo, and Thomas J. Balkin. 2003. “Patterns of Performance Degradation and Restoration During Sleep Restriction and Subsequent Recovery: A Sleep Dose-Response Study.” Journal of Sleep Research 12: 1--12.

Bellman, R. 1957. “A Markovian Decision Process.” Journal of Mathematics and Mechanics 6 (5). http://www.jstor.org/stable/24900506.

Boeckmann, A. J., L. B. Sheiner, and S. L. Beal. 1992. NONMEM Users Guide: Part v, Introductory Guide. NONMEM Project Group, University of California, San Francisco.

Borne, Kirk. 2021. “Association Rule Mining — Not Your Typical ML Algorithm.” Medium. https://medium.com/@kirk.borne/association-rule-mining-not-your-typical-ml-algorithm-97acda6b86c2.

Box, George E. P. 1976. “Science and Statistics.” Journal of the American Statistical Association 71 (356): 791–99.

Breiman, Leo. 1996. “Bagging Predictors.” Machine Learning 24: 123–40.

———. 2001a. “Random Forests.” Machine Learning 45: 5–32.

———. 2001b. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (3): 199–231.

Breiman, L., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees. Wadsworth, Pacific Grove, CA.

Cleveland, William S. 1979. “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association 74 (368): 829–36. https://doi.org/10.1080/01621459.1979.10481038.

Davidian, M., and D. M. Giltinan. 1995. Nonlinear Models for Repeated Measurement Data. Chapman & Hall, London.

Ferrari, S. L. P., and F. Cribari-Neto. 2004. “Beta Regression for Modeling Rates and Proportions.” Journal of Applied Statistics 31 (7): 799–815.

Fraley, Chris, and Adrian E Raftery. 2002. “Model-Based Clustering, Discriminant Analysis, and Density Estimation.” Journal of the American Statistical Association 97 (458): 611–31. https://doi.org/10.1198/016214502760047131.

Friedman, J. H. 2002. “Stochastic Gradient Boosting.” Computational Statistics and Data Analysis 38 (4): 367–78.

Friedman, Jerome, Hastie Trevor, and Robert Tibshirani. 2000. “Additive Logistic Regression: A Statistical View of Boosting.” The Annals of Statistics 28 (2): 337–407.

Furnival, George M., and Robert W. Wilson. 1974. “Regression by Leaps and Bounds.” Technometrics 16 (4): 499–511.

García-Portugués, E. 2024. Notes for Predictive Modeling. https://bookdown.org/egarpor/PM-UC3M/.

Gilliland, D., and O. Schabenberger. 2001. “Limits on Pairwise Association for Equi-Correlated Binary Variables.” Journal of Applied Statistical Sciences 10: 279--285.

Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.

Gower, J. C. n.d. “A General Coefficient of Similarity and Some of Its Properties.” Biometrics 27 (4): 857–71.

Grue, Lars, and Arvid Heiberg. 2006. “Notes on the History of Normality–Reflections on the Work of Quetelet and Galton.” Scandinavian Journal of Disability Research 8 (4): 232–46.

Hartigan, J. A., and M. A. Wong. 1979. “Algorithm AS 136: A k-Means Clustering Algorithm.” Journal of the Royal Statistical Society. Series C (Applied Statistics) 28 (1): 100–108.

Harville, D. A. 1976. “Extension of the Gauss-Markov Theorem to Include the Estimation of Random Effects.” The Annals of Statistics 4: 384–95.

Hastie, T. J., and R. Tibshirani. 1990. Generalized Additive Models. Chapman & Hall, London.

Hastie, Trevor, Robert Tibshirani, and Jerome Friedman. 2001. The Elements of Statistical Learning. Springer Series in Statistics. New York, NY, USA: Springer New York Inc.

Henderson, C. R. 1950. “The Estimation of Genetic Parameters.” The Annals of Mathematical Statistics 21 (2): 309–10.

———. 1984. Applications of Linear Models in Animal Breeding. University of Guelph.

Hinne, Max, Quentin F. Gronau, Don van den Bergh, and Eric-Jan Wagenmakers. 2020. “A Conceptual Introduction to Bayesian Model Averaging.” Advances in Methods and Practices in Psychological Science 3 (2): 200–215. https://doi.org/10.1177/2515245919898657.

James, Gareth, Daniela Witten, Trevor Hastie, and Robert Tibshirani. 2021. An Introduction to Statistical Learning: With Applications in r, 2nd Ed. Springer. https://www.statlearning.com/.

Kleinbaum, David G., Lawrence L. Kupper, A. Nizam, and Eli S. Rosenberg. 2013. Applied Regression Analysis and Other Multivariable Methods, 5 Ed. Cengage Learning.

Little, R., and D. Rubin. 1987. Statistical Analysis with Missing Data. Wiley, New York.

Lundberg, Scott M., Gabriel G. Erion, and Su-In Lee. 2018. “Consistent Individualized Feature Attribution for Tree Ensembles.” https://arxiv.org/abs/1802.03888.

Lundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” 31st Conference on Neural Information Processing Systems (NIPS). https://arxiv.org/abs/1705.07874.

Mallows, C. L. 1973. “Some Comments on C_p.” Technometrics 15 (4): 661–75.

Mazzanti, Samuele. 2020. “SHAP Values Explained Exactly How You Wished Someone Explained to You. Making Sense of the Formula Used for Computing SHAP Values.” Medium. https://towardsdatascience.com/shap-explained-the-way-i-wish-someone-explained-it-to-me-ab81cc69ef30.

McCullagh, P., and J. A. Nelder Frs. 1989. Generalized Linear Models, 2nd Ed. Chapman & Hall, New York.

McCulloch, Warren S., and Walter Pitts. 1943. “A Logical Calculus of the Ideas Immanent in Nervous Activity.” Bulletin of Mathematical Biophysics 5: 115–33.

Mead, R., R. N. Curnow, and A. M. Hasted. 1993. Statistical Methods in Agriculture and Experimental Biology. CRC Press, New York; Boca Raton, FL.

Miller, Alan J. 1984. “Selection of Subsets of Regression Variables.” Journal of the Royal Statistical Society, Series A. 147 (3): 389–425.

Molnar, Christoph. 2022. Interpretable Machine Learning: A Guide for Making Black Box Models Explainable. 2nd ed. https://christophm.github.io/interpretable-ml-book.

Nash, Warwick J., Tracy L. Sellers, Simon R. Talbot, Andrew J. Cawthorn, and Wes B Ford. 1994. “The Population Biology of Abalone (*Haliotis* Species) in Tasmania. I. Blacklip Abalone (*h. Rubra*) from the North Coast and Islands of Bass Strait.”

Pinheiro, J. C., and D. M. Bates. 1995. “Approximations to the Log-Likelihood Function in the Nonlinear Mixed-Effects Model.” Journal of Computational and Graphical Statistics 4: 12–35.

Prater, N. H. 1956. “Estimate Gasoline Yields from Crudes.” Petroleum Refiner 35 (3).

Raftery, Adrian E. 1995. “Bayesian Model Selection in Social Research.” Sociological Methodology 25: 111–63.

Ratkowsky, D. A. 1983. Nonlinear Regression Modeling. Marcel Dekker, New York.

———. 1990. Handbook of Nonlinear Regression Models. Marcel Dekker, New York.

Sankaran, Kris. 2024. “Data Science Principles for Interpretable and Explainable AI.” Journal of Data Science, 1–27. https://doi.org/10.6339/24-JDS1150.

Schabenberger, O., and Francis J. Pierce. 2001. Contemporary Statistical Models for the Plant and Soil Sciences. CRC Press, Boca Raton.

Schabenberger, O., B. E. Tharp, Kells J. J., and D. Penner. 1999. “Statistical Tests for Hormesis and Effective Dosages in Herbicide Dose Response.” Agronomy Journal 91: 713–21.

Shapley, L. 1953. “A Value for n-Peson Games.” In Contributions to the Theory of Games II, edited by N. Kuhn and A. Tucker, 307–17. Princeton University Press, Princeton.

Sutton, Clifton D. 2005. “Classification and Regression Trees, Bagging, and Boosting.” Handbook of Statistics 24: 303–29.

Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. “Attention Is All You Need.” In Proceedings of the 31st International Conference on Neural Information Processing Systems, 6000–6010. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.

Zhang, Grace. 2018. “What Is the Kernel Trick? Why Is It Important?” Medium. https://medium.com/@zxr.nju/what-is-the-kernel-trick-why-is-it-important-98a98db0961d.