Multivariate outlier detection of dairy herd testing data

Authors

  • Ho Chang Choi Department of Mathematics and Statistics, University of Canterbury
  • Howard Paul Edwards Massey University http://orcid.org/0000-0002-9182-9230
  • C Hassell Sweatman Auckland University of Technology
  • V Obolonkin Livestock Improvement Corporation

DOI:

https://doi.org/10.21914/anziamj.v57i0.10512

Keywords:

statistics, multivariate statistics, outlier detection

Abstract

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
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  • 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.

Author Biography

Howard Paul Edwards, Massey University

Senior Lecturer in Statstics

Published

2016-05-30

Issue

Section

Proceedings of the Mathematics in Industry Study Group