References
- Bradley, R. C. (2005), “Basic Properties of Strong Mixing Conditions: A Survey and Some Open Questions,” Probability Surveys, 2, 107–144. DOI: 10.1214/154957805100000104.
- Cortes, C., and Vapnik, V. (1995), “Support-Vector Networks,” Machine Learning, 20, 273–297. DOI: 10.1007/BF00994018.
- Cover, T. M., and Hart, P. E. (1967), “Nearest Neighbor Pattern Classification,” IEEE Transactions on Information Theory, 13, 21–27. DOI: 10.1109/TIT.1967.1053964.
- Geraci, M. (2016), “Qtools: A Collection of Models and Other Tools for Quantile Inference,” R Journal, 8, 117–138. DOI: 10.32614/RJ-2016-037.
- Geraci, M. (2020), Qtools: Utilities for Quantiles. R package version 1.5.2.
- Geraci, M., Boghossian, N. S., Farcomeni, A., and Horbar, J. D. (2020), “Quantile Contours and Allometric Modelling for Risk Classification of Abnormal Ratios With an Application to Asymmetric Growth-Restriction in Preterm Infants,” Statistical Methods in Medical Research, 29, 1769–1786. DOI: 10.1177/0962280219876963.
- Hall, P., Titterington, D. M., and Xue, J.-H. (2009), “Median-Based Classifiers for High-Dimensional Data,” Journal of the American Statistical Society, 104, 1597–1608. DOI: 10.1198/jasa.2009.tm08107.
- Hand, D., and Yu, K. (2001), “Idiot’s Bayes - Not So Stupid After All?” International Statistical Review, 69, 385–398.
- Hennig, C., and C. Viroli (2016a), “Quantile-Based Classifiers,” Biometrika, 103, 435–446. DOI: 10.1093/biomet/asw015.
- Hennig, C., and C. Viroli (2016b), quantileDA: Quantile Classifier. R package version 1.1.
- Kong, L., and Mizera, I. (2012), “Quantile Tomography: Using Quantiles With Multivariate Data,” Statistica Sinica, 22, 1589–1610. DOI: 10.5705/ss.2010.224.
- Lai, Y., and McLeod, I. (2019), eqc: Ensemble quantile classification. R package version 1.2-2.
- Lai, Y., and McLeod, I. (2020), “Ensemble Quantile Classifier,” Computational Statistics & Data Analysis, 144, 106849.
- Lee, E.-K., Cook, D., Klinke, S., and Lumley, T. (2005), “Projection Pursuit for Exploratory Supervised Classification,” Journal of Computational Graphical Statistics, 14, 831–846. DOI: 10.1198/106186005X77702.
- Meyer, D., Dimitriadou, E., Hornik, K., Weingessel, A., and Leisch, F. (2019), e1071: Misc Functions of the Department of Statistics, Probability Theory Group (Formerly: E1071), TU Wien. R package version 1.7-3.
- Park, M. Y., and Hastie, T. (2008), “Penalized Logistic Regression for Detecting Gene Interactions,” Biostatistics, 9, 30–50. DOI: 10.1093/biostatistics/kxm010.
- Park, M. Y., and Hastie, T. (2018), stepPlr: L2 Penalized Logistic Regression With Stepwise Variable Selection. R package version 0.93.
- R Core Team (2020), R: A Language and Environment for Statistical Computing, Vienna, Austria: R Foundation for Statistical Computing.
- Sorrentino, D., Avellini, C., Geraci, M., Dassopoulos, T., Zarifi, D., Vadalá di Prampero, S. F., and Benevento, G. (2014), “Tissue Studies in Screened First-Degree Relatives Reveal a Distinct Crohn’s Disease Phenotype,” Inflammatory Bowel Diseases, 20, 1049–1056.
- Stam, A. J. (1982), “Limit Theorems for Uniform Distributions on Spheres in High-Dimensional Euclidean Spaces,” Journal of Applied Probabability, 19, 221–228. DOI: 10.2307/3213932.
- Tibshirani, R., Hastie, T., Narasimhan, B., and Chu, G. (2002), “Diagnosis of Multiple Cancer Types by Shrunken Centroids of Gene Expression,” Proceedings of the National Academy of Sciences of the United States of America, 99, 6567–6572. DOI: 10.1073/pnas.082099299.
- Venables, W. N., and Ripley, B. D. (2002), Modern Applied Statistics With S (4th ed.). New York: Springer.
- Wang, L., Zhu, J., and Zou, H. (2008), “Hybrid Huberized Support Vector Machines for Microarray Classification and Gene Selection,” Bioinformatics, 24, 412–419. DOI: 10.1093/bioinformatics/btm579.