Abrams, B. 2003. “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.
Bartley, E AND Schliep, M . AND Hanks. 2019. “Identifying and Characterizing Extrapolation in Multivariate Response Data.” PLOS ONE 14 (December): 1–20.
Biecek, Przemyslaw, and Tomasz Burzykowski. 2021. Explanatory Model Analysis. Chapman; Hall/CRC, New York.
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. 1996a. “Bagging Predictors.” Machine Learning 24 (2): 123–40.
———. 1996b. “Stacked Regressions.” Machine Learning 24 (1): 49–64.
———. 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.
Claeskens, G. 2016. “Statistical Model Choice.” Annual Review of Statistics and Its Application 3: 233–56.
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.
Danowski, T, J Aarons, J Hydovitz, and J Wingert. 1970. “Utility of Equivocal Glucose Tolerances.” Diabetes 19 (7): 524–26.
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.”
Freund, Y, and R Schapire. 1997. “A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting.” Journal of Computer and System Sciences 55 (1): 119–39.
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.
Good, I. J. 1985. “Weight of Evidence: A Brief Survey.” Bayesian Statistics 2: 249–70.
Goodfellow, I, Y Bengio, and A Courville. 2016. Deep Learning. MIT Press.
Guo, Cheng, and Felix Berkhahn. 2016. “Entity Embeddings of Categorical Variables.”
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.
Ho, T. 1995. “Random Decision Forests.” In Proceedings of 3rd International Conference on Document Analysis and Recognition, 1:278–82. IEEE.
Hosmer, D, and Sy Lemeshow. 2000. Applied Logistic Regression. New York: John Wiley; Sons.
Hvitfeldt, E., and J. Silge. 2021. Supervised Machine Learning for Text Analysis in r. A Chapman & Hall Book. CRC Press.
Hyndman, R, and G Athanasopoulos. 2018. Forecasting: Principles and Practice. OTexts.
Ismay, C, and A Kim. 2021. Statistical Inference via Data Science: A ModernDive into r and the Tidyverse. Chapman; Hall/CRC.
Jaworska, J, N Nikolova-Jeliazkova, and T Aldenberg. 2005. “QSAR Applicability Domain Estimation by Projection of the Training Set in Descriptor Space: A Review.” Alternatives to Laboratory Animals 33 (5): 445–59.
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.
Kerleguer, A., J.-L. Koeck, M. Fabre, P. Gérôme, R. Teyssou, and V. Hervé. 2003. “Use of Equivocal Zone in Interpretation of Results of the Amplified Mycobacterium Tuberculosis Direct Test for Diagnosis of Tuberculosis.” Journal of Clinical Microbiology 41 (4): 1783–84.
Kirkpatrick, S, D Gelatt, and M Vecchi. 1983. “Optimization by Simulated Annealing.” Science 220 (4598): 671–80.
Koklu, M, and IA Ozkan. 2020. “Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.” Computers and Electronics in Agriculture 174: 105507.
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.”
Kuhn, M, and K Johnson. 2013. Applied Predictive Modeling. Springer.
———. 2020. Feature Engineering and Selection: A Practical Approach for Predictive Models. CRC Press.
Lambert, D. 1992. “Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing.” Technometrics 34 (1): 1–14.
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.
Long, J. 1992. Measures of Sex Differences in Scientific Productivity*.” Social Forces 71 (1): 159–78.
Lundberg, Scott M., and Su-In Lee. 2017. “A Unified Approach to Interpreting Model Predictions.” In Proceedings of the 31st International Conference on Neural Information Processing Systems, 4768–77. NIPS’17. Red Hook, NY, USA: Curran Associates Inc.
Mangiafico, S. 2015. “An R Companion for the Handbook of Biological Statistics.”
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.
McCullagh, P, and J Nelder. 1989. Generalized Linear Models. London: Chapman; Hall.
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.
McInnes, L, J Healy, and J Melville. 2020. “UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction.”
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.
Micci-Barreca, Daniele. 2001. “A Preprocessing Scheme for High-Cardinality Categorical Attributes in Classification and Prediction Problems.” SIGKDD Explor. Newsl. 3 (1): 27–32.
Mingqiang, Y, K Kidiyo, and R Joseph. 2008. “A Survey of Shape Feature Extraction Techniques.” In Pattern Recognition, edited by PY Yin. Rijeka: IntechOpen.
Molnar, Christopher. 2020. Interpretable Machine Learning.
Mullahy, J. 1986. “Specification and Testing of Some Modified Count Data Models.” Journal of Econometrics 33 (3): 341–65.
Netzeva, T, A Worth, T Aldenberg, R Benigni, M Cronin, P Gramatica, J Jaworska, et al. 2005. “Current Status of Methods for Defining the Applicability Domain of (Quantitative) Structure-Activity Relationships: The Report and Recommendations of ECVAM Workshop 52.” Alternatives to Laboratory Animals 33 (2): 155–73.
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.”
R Core Team. 2014. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing.
Rasmussen, C, and C Williams. 2006. Gaussian Processes for Machine Learning. Gaussian Processes for Machine Learning. MIT Press.
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.
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.
Symons, S, and RG Fulcher. 1988. “Determination of Wheat Kernel Morphological Variation by Digital Image Analysis: I. Variation in Eastern Canadian Milling Quality Wheats.” Journal of Cereal Science 8 (3): 211–18.
Thomas, R, and D Uminsky. 2020. “The Problem with Metrics Is a Fundamental Problem for AI.”
Tibshirani, Robert. 1996. “Regression Shrinkage and Selection via the Lasso.” Journal of the Royal Statistical Society. Series B (Methodological) 58 (1): 267–88.
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.
Weinberger, K, A Dasgupta, J Langford, A Smola, and J Attenberg. 2009. “Feature Hashing for Large Scale Multitask Learning.” In Proceedings of the 26th Annual International Conference on Machine Learning, 1113–20. ACM.
Wickham, H. 2019. Advanced r. 2nd ed. Chapman & Hall/CRC the r Series. Taylor & Francis.
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.
Wolpert, D. 1992. “Stacked Generalization.” Neural Networks 5 (2): 241–59.
Wu, X, and Z Zhou. 2017. “A Unified View of Multi-Label Performance Measures.” In International Conference on Machine Learning, 3780–88.
Wundervald, B, A Parnell, and K Domijan. 2020. “Generalizing Gain Penalization for Feature Selection in Tree-Based Models.”
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.
Zeileis, A, C Kleiber, and S Jackman. 2008. “Regression Models for Count Data in R.” Journal of Statistical Software 27 (8): 1–25.
Zumel, Nina, and John Mount. 2019. “Vtreat: A Data.frame Processor for Predictive Modeling.”