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Multivariate non-parametric tests of trend when the data are incomplete

✍ Scribed by Mayer Alvo; Jincheol Park

Publisher: Elsevier Science
Year: 2002
Tongue: English
Weight: 112 KB
Volume: 57
Category: Article
ISSN: 0167-7152
DOI: 10.1016/s0167-7152(02)00062-7

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✦ Synopsis

In environmental and medical studies, multivariate data are often recorded over regular time intervals and examined for monotone increasing or decreasing trends in one or more of the variables. Dietz and Killeen (J. Amer. Statist. Assoc. 76 (1981) 169) proposed a non-parametric test based on the Kendall measure of correlation and applied it to medical data. In this paper, we are concerned with situations when the data are partially incomplete. New test statistics based on the Spearman and Kendall correlation coe cients are proposed which are shown to be asymptotically chi squared. Results from a limited simulation study reveal that in most situations, the proposed test statistic performs better than its counterpart which deletes the missing data.

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