Multivariate measures of association are considered which, in the bivariate case, coincide with the population version of Spearman’s rho. For these measures, nonparametric estimators are introduced via the empirical copula. Their asymptotic normality is established under rather weak assumptions concerning the copula. The asymptotic variances are explicitly calculated for some copulas of simple ...

This is an expository account of the edge eigenvalue distributions in random matrix theory and their application in multivariate statistics. The emphasis is on the Painlevé representations of these distribution functions.

We propose two new dependent randomized rounding algorithms for approximating the global maximum of a quadratic program subject to linear as well as boolean constraints. We show that the rst of these methods based on the multivariate normal distribution, provides (a) a 0.878 approximation algorithm for s ?t max-cut problems, (b) a 0.878 approximation algorithm for the max-cut problem that has t...

A second-order method for blind source separation of noisy instantaneous linear mixtures is presented and analyzed for the case where the signal order k and noise covariance GGH are unknown. Only a data set X of dimension n> k and of sample size m is observed, where X = AP + GW. The quality of separation depends on source-observation ratio }, the degree of spectral diversity, and the second-ord...

The classical univariate sign and signed rank tests for location have been extended over the years to the multivariate setting, including recent robust rotation invariant “spatial” versions. Here we introduce a broad class of rotation invariant multivariate spatial generalized rank type test statistics. For a given inference problem not restricted to location, the test statistics are linked thr...

We review three recently proposed scan statistic methods for multivariate pattern detection. Each method models the relationship between multiple observed and hidden variables using a Bayesian network structure, drawing inferences about the underlying pattern type and the affected subset of the data. We first discuss the multivariate Bayesian scan statistic (MBSS) proposed by Neill and Cooper (...

We propose several multivariate variance ratio statistics. We derive the asymptotic distribution of the statistics and scalar functions thereof under the null hypothesis that returns are unpredictable after a constant mean adjustment (i.e., under the Efficient Market Hypothesis). We do not impose the no leverage assumption of Lo and MacKinlay (1988) but our asymptotic standard errors are relati...

This study proposed a novel methodology to classify the shape of gaps using landscape indices and multivariate statistics. Patch-level indices were used to collect the qualified shape and spatial configuration characteristics for canopy gaps in the Lienhuachih Experimental Forest in Taiwan in 1998 and 2002. Non-hierarchical cluster analysis was used to assess the optimal number of gap clusters ...

Let X1;X2;...;Xn have a jointly multivariate exchangeable normal distribution. In this work we investigate another proof of the independence of X and S2 using order statistics. We also assume that (Xi ; Yi); i =1; 2;...; n; jointly distributed in bivariate normal and establish the independence of the mean and the variance of concomitants of order statistics.

The product moments of existing and new noncentral bimatrix variate beta distributions with bounded domain are derived. From these, exact expressions for the distributions of statistics are obtained by using the Mellin transform. These distributions add value to multivariate statistical analysis with specific reference to factors of Wilks’ statistics and the product of generalized statistics.