A comparison of multivariate outlier detection methods for clinical laboratory safety data

Journal of the Royal Statistical Society Series D: The Statistician

Volumn 50, Issue 3, 2001, Pages 295-307

  Penny, Kay I ^a   Jolliffe, Ian T ^b  

^a EDINBURGH NAPIER UNIVERSITY   (United Kingdom)

^b Napier University ^*

Author keywords

Laboratory safety data; Multivariate outlier detection; Simulation study

Indexed keywords

EID: 0040481253     PISSN: 00390526     EISSN: None     Source Type: Journal    
DOI: 10.1111/1467-9884.00279     Document Type: Article

References (15)

1. Barnett, V. and Lewis, T. (1994) Outliers in Statistical Data, 3rd edn. New York: Wiley.
2. Campbell, N. A. (1980) Robust procedures in multivariate analysis: I, Robust covariance estimation. Appl. Statist., 29, 231-237.
3. Caroni, C. and Prescott, P. (1992) Sequential application of Wilk's multivariate outlier test. Appl. Statist., 41, 355-364.
4. Caussinus, H. and Ruiz, A. (1990) Interesting projections of multidimensional data by means of generalized principal component analysis. In Compstat 90, pp. 121-126. Heidelberg: Physica.
5. Chuang-Stein, C. (1992) Safety analysis: too much?, not enough? and how? Biopharm. Rep., 1, no. 3.
6. Davies, J. M. (1994) Current reporting methods for laboratory data at Zeneca Pharmaceuticals. Drug Inform. J., 28, 403-406.
7. Davies, L. and Gather, U. (1993) The identification of multiple outliers. J. Am. Statist. Ass., 88, 782-801.
8. Efron, B. (1979) Bootstrap methods: another look at the jackknife. Ann. Statist., 7, 1-26.
9. Efron, B. and Tibshirani, R. J. (1993) An Introduction to the Bootstrap. New York: Chapman and Hall.
10. Grubbs, F. E. (1969) Procedures for detecting outlying observations in samples. Technometrics, 11, 1-21.
11. Hadi, A. S. (1992) Identifying multiple outliers in multivariate data. J. R. Statist. Soc. B, 54, 761-771.
12. Hawkins, D. M. (1980) Identification of Outliers. London: Chapman and Hall.
13. Penny, K. I. (1996) Appropriate critical values when testing for a single multivariate outlier by using the Mahalanobis distance. Appl. Statist., 45, 73-81.
14. Penny, K. I. and Jolliffe, I. T. (1995) Multivariate outlier detection in clinical laboratory safety data. Technical Report 95115. Department of Mathematical Sciences, University of Aberdeen, Aberdeen.
15. Schiffman, S. S., Reynolds, M. L. and Young, F. W. (1981) Introduction to Multidimensional Scaling: Theory, Methods, and Applications. New York: Academic Press.