This paper describes the challenge presented by the Livestock Improvement Corporation regarding the need to detect multivariate outliers in very large datasets of dairy herd milk testing data. Various approaches and techniques were applied to a subset of one dataset in order to establish the potential of both manual and automatic detection of outliers in large datasets using multivariate statistical techniques. References P. Filzmoser, R. G. Garrett and C. Reimann. Multivariate outlier detection in exploratory geochemistry. Computers and Geosciences, 31:579–587, 2005. P. Filzmoser, R. Maronna and M. Werner. Outlier identification in high dimensions. Computational Statistics and Data Analysis, 52:1694–1711, 2008. E. Gilleland, M. Ribatet and A. G. Stephenson. A software review for extreme value analysis. Extremes, 16:103–119, 2013. P Rousseeuw. Multivariate estimation with high breakdown point. In W. Grossmann, G. Pflug, I. Vincze, W. Wertz, editor, Mathematical Statistics and Applications Volume B, pages 283–297, Budapest, 1985. Akademiai Kiado.
@article{10512,
title = {Multivariate outlier detection of dairy herd testing data},
journal = {ANZIAM Journal},
volume = {56},
year = {2016},
doi = {10.21914/anziamj.v57i0.10512},
language = {EN},
url = {http://dml.mathdoc.fr/item/10512}
}
Choi, Ho Chang; Edwards, Howard Paul; Sweatman, C Hassell; Obolonkin, V. Multivariate outlier detection of dairy herd testing data. ANZIAM Journal, Tome 56 (2016) . doi : 10.21914/anziamj.v57i0.10512. http://gdmltest.u-ga.fr/item/10512/