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

Mayer Alvo and Jincheol Park

Statistics & Probability Letters, 2002, vol. 57, issue 3, 281-290

Abstract: 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 coefficients 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.

Keywords: Rankings; Spearman; Kendall; Missing; observations; Multivariate; data (search for similar items in EconPapers)
Date: 2002
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