Multivariate non-parametric tests of trend when the data are incomplete
AbstractIn 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Elsevier in its journal Statistics & Probability Letters.
Volume (Year): 57 (2002)
Issue (Month): 3 (April)
Contact details of provider:
Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Patel, Kantilal M., 1973. "Hájek-Sidák approach to the asymptotic distribution of multivariate rank order statistics," Journal of Multivariate Analysis, Elsevier, vol. 3(1), pages 57-70, March.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wendy Shamier).
If references are entirely missing, you can add them using this form.