Journals on Biomedical Engineering - BJSTR Journal

Abstract

This paper presents a modified #chi-square approximation to the distribution of test statistics arising from multivariate ranked data. The modification arises from an improvement to the estimated variance matrix of the responses and from corrections for continuity and skewness and kurtosis of the rank sum statistics.#Kawaguchi et al. consider tests of equality of means for multivariate responses, in the presence of covariates, stratification, and tied and missing data. They propose inference based on approximating the joint distribution of #Wilcoxon rank sum statistics as multivariate normal, with an estimated covariance matrix. They present a test statistic that may be expressed as a quadratic form of Wilcoxon rank sum statistics, with the variance-covariance matrix estimated using methods derived by Davis and #Quade; they also apply this multivariate normal approximation to derive univariate confidence intervals. In this paper, we examine the effect of some alternative variance matrix estimates and also investigate the usefulness of the approximation of #Yarnold, correcting for discreteness, skewness and #kurtosis [1-3].

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 Various Improved Approximations to Distributions of Quadratic Test Statistics for Dependent Rank Sums by John E Kolassa in BJSTR