We considered NxN Wishart ensembles in the class (complex Wishart matrices with M degrees of freedom and covariance matrix [Sigma]N) such that N0 eigenvalues of [Sigma]N are 1 and N1=N-N0 of them are a. We studied the limit as M, N, N0 and N1 all go to infinity such that , and 0c,[beta]1. In...

Let be generalized order statistics based on a continuous distribution function F with parameters k and (m1,...,mn-1). Chen and Hu (2007) [8] investigated the sufficient conditions on F and on the parameters k and mi's such that , where , and is the Shaked-Shanthikumar multivariate dispersive...

This paper considers the nonparametric M-estimator in a nonlinear cointegration type model. The local time density argument, which was developed by Phillips and Park (1998) [6] and Wang and Phillips (2009) [9], is applied to establish the asymptotic theory for the nonparametric...

For a normal random matrix Y with mean zero, necessary and sufficient conditions are obtained for Y'WkY to be Wishart-Laplace distributed and {Y'WkY} to be independent, where each Wk is assumed to be symmetric rather than nonnegative definite.

In this paper, a new measure of dependence is proposed. Our approach is based on transforming univariate data to the space where the marginal distributions are normally distributed and then, using the inverse transformation to obtain the distribution function in the original space. The...

We find the asymptotic distribution of the OLS estimator of the parameters [beta] and [rho] in the mixed spatial model with exogenous regressors Yn=Xn[beta]+[rho]WnYn+Vn. The exogenous regressors may be bounded or growing, like polynomial trends. The assumption about the spatial matrix Wn is...

How to take advantage of the available auxiliary covariate information when the primary covariate of interest is not measured is a frequently encountered question in biomedical study. In this paper, we consider the multivariate failure times regression analysis in which the primary covariate is...

In this paper we consider exact tests of a multiple logistic regression with categorical covariates via Markov bases. In many applications of multiple logistic regression, the sample size is positive for each combination of levels of the covariates. In this case we do not need a whole Markov...

The concept of quadratic subspace is introduced as a helpful tool for dimension reduction in quadratic discriminant analysis (QDA). It is argued that an adequate representation of the quadratic subspace may lead to better methods for both data representation and classification. Several...

Comparison of nonparametric regression models has been extensively discussed in the literature for the one-dimensional covariate case. The comparison problem largely remains open for completely nonparametric models with multi-dimensional covariates. We address this issue under the assumption...