Abrams, B. 2003. “The Pit of Success.” https://blogs.msdn.microsoft.com/brada/2003/10/02/the-pit-of-success/.
Baggerly, K, and K Coombes. 2009. “Deriving Chemosensitivity from Cell Lines: Forensic Bioinformatics and Reproducible Research in High-Throughput Biology.” The Annals of Applied Statistics 3 (4): 1309–34.
Bohachevsky, I, M Johnson, and M Stein. 1986. “Generalized Simulated Annealing for Function Optimization.” Technometrics 28 (3): 209–17.
Bolstad, B. 2004. Low-Level Analysis of High-Density Oligonucleotide Array Data: Background, Normalization and Summarization. University of California, Berkeley.
Box, GEP, W Hunter, and J Hunter. 2005. Statistics for Experimenters: An Introduction to Design, Data Analysis, and Model Building. Wiley.
Bradley, R, and M Terry. 1952. “Rank Analysis of Incomplete Block Designs: I. The Method of Paired Comparisons.” Biometrika 39 (3/4): 324–45.
Breiman, L. 2001a. “Random Forests.” Machine Learning 45 (1): 5–32.
———. 2001b. “Statistical Modeling: The Two Cultures.” Statistical Science 16 (3): 199–231.
Carlson, B. 2012. “Putting Oncology Patients at Risk.” Biotechnology Healthcare 9 (3): 17–21.
Chambers, J. 1998. Programming with Data: A Guide to the S Language. Berlin, Heidelberg: Springer-Verlag.
Chambers, J, and T Hastie, eds. 1992. Statistical Models in S. Boca Raton, FL: CRC Press, Inc.
Cleveland, W. 1979. “Robust Locally Weighted Regression and Smoothing Scatterplots.” Journal of the American Statistical Association 74 (368): 829–36.
Craig–Schapiro, R, M Kuhn, C Xiong, E Pickering, J Liu, T Misko, R Perrin, et al. 2011. “Multiplexed Immunoassay Panel Identifies Novel CSF Biomarkers for Alzheimer’s Disease Diagnosis and Prognosis.” PLoS ONE 6 (4): e18850.
Cybenko, G. 1989. “Approximation by Superpositions of a Sigmoidal Function.” Mathematics of Control, Signals and Systems 2 (4): 303–14.
Davison, A, and D Hinkley. 1997. Bootstrap Methods and Their Application. Vol. 1. Cambridge university press.
De Cock, D. 2011. “Ames, Iowa: Alternative to the Boston Housing Data as an End of Semester Regression Project.” Journal of Statistics Education 19 (3).
Dobson, A. 1999. An Introduction to Generalized Linear Models. Chapman; Hall: Boca Raton.
Durrleman, S, and R Simon. 1989. “Flexible Regression Models with Cubic Splines.” Statistics in Medicine 8 (5): 551–61.
Faraway, J. 2016. Extending the Linear Model with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. CRC press.
Fox, J. 2008. Applied Regression Analysis and Generalized Linear Models. Second. Thousand Oaks, CA: Sage.
Frazier, R. 2018. “A Tutorial on Bayesian Optimization.” http://arxiv.org/abs/1807.02811.
Friedman, J. 1991. “Multivariate Adaptive Regression Splines.” The Annals of Statistics 19 (1): 1–141.
———. 2001. “Greedy Function Approximation: A Gradient Boosting Machine.” Annals of Statistics 29 (5): 1189–1232.
Friedman, J, T Hastie, and R Tibshirani. 2010. “Regularization Paths for Generalized Linear Models via Coordinate Descent.” Journal of Statistical Software 33 (1): 1.
Geladi, P., and B Kowalski. 1986. “Partial Least-Squares Regression: A Tutorial.” Analytica Chimica Acta 185: 1–17.
Gentleman, R, V Carey, W Huber, R Irizarry, and S Dudoit. 2005. Bioinformatics and Computational Biology Solutions Using R and Bioconductor. Berlin, Heidelberg: Springer-Verlag.
Goodfellow, I, Y Bengio, and A Courville. 2016. Deep Learning. MIT Press.
Hand, D, and R Till. 2001. “A Simple Generalisation of the Area Under the ROC Curve for Multiple Class Classification Problems.” Machine Learning 45 (August): 171–86.
Hill, A, P LaPan, Y Li, and S Haney. 2007. “Impact of Image Segmentation on High-Content Screening Data Quality for SK-BR-3 Cells.” BMC Bioinformatics 8 (1): 340.
Hosmer, D, and Sy Lemeshow. 2000. Applied Logistic Regression. New York: John Wiley; Sons.
Hyndman, R, and G Athanasopoulos. 2018. Forecasting: Principles and Practice. OTexts.
Johnson, D, P Eckart, N Alsamadisi, H Noble, C Martin, and R Spicer. 2018. “Polar Auxin Transport Is Implicated in Vessel Differentiation and Spatial Patterning During Secondary Growth in Populus.” American Journal of Botany 105 (2): 186–96.
Joseph, V, E Gul, and S Ba. 2015. “Maximum Projection Designs for Computer Experiments.” Biometrika 102 (2): 371–80.
Jungsu, K, D Basak, and D Holtzman. 2009. “The Role of Apolipoprotein E in Alzheimer’s Disease.” Neuron 63 (3): 287–303.
Kirkpatrick, S, D Gelatt, and M Vecchi. 1983. “Optimization by Simulated Annealing.” Science 220 (4598): 671–80.
Krueger, T, D Panknin, and M Braun. 2015. “Fast Cross-Validation via Sequential Testing.” Journal of Machine Learning Research 16 (33): 1103–55.
Kruschke, J, and T Liddell. 2018. “The Bayesian New Statistics: Hypothesis Testing, Estimation, Meta-Analysis, and Power Analysis from a Bayesian Perspective.” Psychonomic Bulletin and Review 25 (1): 178–206.
Kuhn, Max. 2014. “Futility Analysis in the Cross-Validation of Machine Learning Models.” http://arxiv.org/abs/1405.6974.
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
———. 2020. Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press.
Littell, R, J Pendergast, and R Natarajan. 2000. “Modelling Covariance Structure in the Analysis of Repeated Measures Data.” Statistics in Medicine 19 (13): 1793–1819.
Mangiafico, S. 2015. “An R Companion for the Handbook of Biological Statistics.” https://rcompanion.org/handbook/.
Maron, O, and A Moore. 1994. “Hoeffding Races: Accelerating Model Selection Search for Classification and Function Approximation.” In Advances in Neural Information Processing Systems, 59–66.
McDonald, J. 2009. Handbook of Biological Statistics. Sparky House Publishing.
McElreath, R. 2020. Statistical Rethinking: A Bayesian Course with Examples in R and Stan. CRC press.
McKay, M, R Beckman, and W Conover. 1979. “A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code.” Technometrics 21 (2): 239–45.
Olsson, D, and L Nelson. 1975. “The Nelder-Mead Simplex Procedure for Function Minimization.” Technometrics 17 (1): 45–51.
Opitz, J, and S Burst. 2019. “Macro F1 and Macro F1.” http://arxiv.org/abs/1911.03347.
Rasmussen, C, and C Williams. 2006. Gaussian Processes for Machine Learning. Gaussian Processes for Machine Learning. MIT Press.
R Core Team. 2014. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/.
Santner, T, B Williams, W Notz, and B Williams. 2003. The Design and Analysis of Computer Experiments. Springer.
Schmidberger, M, M Morgan, D Eddelbuettel, H Yu, L Tierney, and U Mansmann. 2009. “State of the Art in Parallel Computing with R.” Journal of Statistical Software 31 (1): 1–27. https://www.jstatsoft.org/v031/i01.
Schulz, E, M Speekenbrink, and A Krause. 2018. “A Tutorial on Gaussian Process Regression: Modelling, Exploring, and Exploiting Functions.” Journal of Mathematical Psychology 85: 1–16.
Shahriari, B., K. Swersky, Z. Wang, R. P. Adams, and N. de Freitas. 2016. “Taking the Human Out of the Loop: A Review of Bayesian Optimization.” Proceedings of the IEEE 104 (1): 148–75.
Shewry, M, and H Wynn. 1987. “Maximum Entropy Sampling.” Journal of Applied Statistics 14 (2): 165–70.
Shmueli, G. 2010. “To Explain or to Predict?” Statistical Science 25 (3): 289–310.
Thomas, R, and D Uminsky. 2020. “The Problem with Metrics Is a Fundamental Problem for Ai.” http://arxiv.org/abs/2002.08512.
Van Laarhoven, P, and E Aarts. 1987. “Simulated Annealing.” In Simulated Annealing: Theory and Applications, 7–15. Springer.
Wasserstein, R, and N Lazar. 2016. “The ASA Statement on P-Values: Context, Process, and Purpose.” The American Statistician 70 (2): 129–33.
Wickham, H, M Averick, J Bryan, W Chang, L McGowan, R François, G Grolemund, et al. 2019. “Welcome to the Tidyverse.” Journal of Open Source Software 4 (43).
Wickham, H, and G Grolemund. 2016. R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media, Inc.
Wu, X, and Z Zhou. 2017. “A Unified View of Multi-Label Performance Measures.” In International Conference on Machine Learning, 3780–8.
Wundervald, B, A Parnell, and K Domijan. 2020. “Generalizing Gain Penalization for Feature Selection in Tree-Based Models.” http://arxiv.org/abs/2006.07515.
Xu, Q, and Y Liang. 2001. “Monte Carlo Cross Validation.” Chemometrics and Intelligent Laboratory Systems 56 (1): 1–11.
Yeo, I-K, and R Johnson. 2000. “A New Family of Power Transformations to Improve Normality or Symmetry.” Biometrika 87 (4): 954–59.