Références

Auerbach, B. M. (n.d.). Goldman Osteometric Data Set [Dr. {{Auerbach}}'s Personal Website].
Auerbach, B. M., & Ruff, C. B. (2004). Human body mass estimation: A comparison of morphometric and mechanical methods. American Journal of Physical Anthropology, 125(4), 331–342. https://doi.org/10.1002/ajpa.20032
Breiman, L. (2001). Random Forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
Cortes, D. (2019). Isotree: Isolation-Based Outlier Detection (pp. 0.6.1–5). Comprehensive R Archive Network. https://doi.org/10.32614/CRAN.package.isotree
Doksum, K. A., & Sievers, G. L. (1976). Plotting with confidence: Graphical comparisons of two populations. Biometrika, 63(3), 421–434. https://doi.org/10.1093/biomet/63.3.421
Fisher, R. A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Gupta, A. S. (2014). Generalized Variance. In R. S. Kenett, N. T. Longford, W. W. Piegorsch, & F. Ruggeri (Eds.), Wiley StatsRef: Statistics Reference Online (1st ed.). Wiley. https://doi.org/10.1002/9781118445112.stat01987
Harrell, F. E., & Davis, C. E. (1982). A new distribution-free quantile estimator. Biometrika, 69(3), 635–640. https://doi.org/10.1093/biomet/69.3.635
Hawkins, D. M. (1980). Identification of Outliers. Springer Netherlands.
Hubert, M., Debruyne, M., & Rousseeuw, P. J. (2018). Minimum covariance determinant and extensions. Wiley Interdisciplinary Reviews: Computational Statistics, 10(3), e1421. https://doi.org/10.1002/wics.1421
Kimber, A. C. (1990). Exploratory Data Analysis for Possibly Censored Data from Skewed Distributions. Journal of the Royal Statistical Society: Series C (Applied Statistics), 39(1), 21–30. https://doi.org/10.2307/2347808
Leys, C., Klein, O., Dominicy, Y., & Ley, C. (2018). Detecting multivariate outliers: Use a robust variant of the Mahalanobis distance. Journal of Experimental Social Psychology, 74, 150–156. https://doi.org/10.1016/j.jesp.2017.09.011
Leys, C., Ley, C., Klein, O., Bernard, P., & Licata, L. (2013). Detecting outliers: Do not use standard deviation around the mean, use absolute deviation around the median. Journal of Experimental Social Psychology, 49(4), 764–766. https://doi.org/10.1016/j.jesp.2013.03.013
Lightfoot, E., Šlaus, M., & O’Connell, T. C. (2014). Water consumption in Iron Age, Roman, and Early Medieval Croatia. American Journal of Physical Anthropology, 154(4), 535–543. https://doi.org/10.1002/ajpa.22544
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2012). Isolation-Based Anomaly Detection. ACM Transactions on Knowledge Discovery from Data, 6(1), 1–39. https://doi.org/10.1145/2133360.2133363
Nikita, E., & Nikitas, P. (2020). Sex estimation: A comparison of techniques based on binary logistic, probit and cumulative probit regression, linear and quadratic discriminant analysis, neural networks, and naïve Bayes classification using ordinal variables. International Journal of Legal Medicine, 134(3), 1213–1225. https://doi.org/10.1007/s00414-019-02148-4
Raymaekers, J., Rousseeuw, P. J., Van den Bossche, W., & Hubert, M. (2020). cellWise: Analyzing Data with Cellwise Outliers. R package version 2.1.1.
Rousseeuw, P. J., & Bossche, W. V. D. (2018). Detecting Deviating Data Cells. Technometrics, 60(2), 135–145. https://doi.org/10.1080/00401706.2017.1340909
Rousseeuw, P. J., & Croux, C. (1993). Alternatives to the Median Absolute Deviation. Journal of the American Statistical Association, 88(424), 1273–1283. https://doi.org/10.1080/01621459.1993.10476408
Rousseeuw, P. J., Ruts, I., & Tukey, J. W. (1999). The Bagplot: A Bivariate Boxplot. The American Statistician, 53(4), 382–387. https://doi.org/10.1080/00031305.1999.10474494
Rousseeuw, P. J., & Van Driessen, K. (1999). A Fast Algorithm for the Minimum Covariance Determinant Estimator. Technometrics, 41(3), 212–223. https://doi.org/10.1080/00401706.1999.10485670
Rousselet, G. A., Pernet, C. R., & Wilcox, R. R. (2017). Beyond differences in means: Robust graphical methods to compare two groups in neuroscience. The European Journal of Neuroscience, 46(2), 1738–1748. https://doi.org/10.1111/ejn.13610
Santos, F. (2020). Modern methods for old data: An overview of some robust methods for outliers detection with applications in osteology. Journal of Archaeological Science: Reports, 32, 102423. https://doi.org/10.1016/j.jasrep.2020.102423
Santos, F., Guyomarc’h, P., & Bruzek, J. (2014). Statistical sex determination from craniometrics: Comparison of linear discriminant analysis, logistic regression, and support vector machines. Forensic Science International, 245, 204.e1–204.e8. https://doi.org/10.1016/j.forsciint.2014.10.010
Todorov, V., & Filzmoser, P. (2009). An Object-Oriented Framework for Robust Multivariate Analysis. Journal of Statistical Software, 32(1), 1–47. https://doi.org/10.18637/jss.v032.i03
Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley Pub. Co.
Unwin, A. (2019). Multivariate Outliers and the O3 Plot. Journal of Computational and Graphical Statistics, 28(3), 635–643. https://doi.org/10.1080/10618600.2019.1575226
Vakili, K., & Schmitt, E. (2013). Finding Multivariate Outliers With FastPCS (arXiv:1301.2053). arXiv. https://doi.org/10.48550/arXiv.1301.2053
Venables, W. N., & Ripley, B. D. (2010). Modern Applied Statistics with S (4. ed., [Nachdr.]). Springer.
Wilcox, R. R. (1995). Comparing Two Independent Groups Via Multiple Quantiles. Journal of the Royal Statistical Society. Series D (The Statistician), 44(1), 91–99. https://doi.org/10.2307/2348620
Wilcox, R. R., Erceg-Hurn, D. M., Clark, F., & Carlson, M. (2014). Comparing two independent groups via the lower and upper quantiles. Journal of Statistical Computation and Simulation, 84(7), 1543–1551. https://doi.org/10.1080/00949655.2012.754026
Willems, G., Joe, H., & Zamar, R. (2009). Diagnosing Multivariate Outliers Detected by Robust Estimators. Journal of Computational and Graphical Statistics, 18(1), 73–91. https://www.jstor.org/stable/25703554