It is well-known that a conditional independence statement for discrete variables is equivalent to constraining to zero a suitable set of log-linear interactions. In this paper we show that this is also equivalent to zero constraints on suitable sets of marginal log-linear interactions, that can...

Density estimation, especially multivariate density estimation, is a fundamental problem in nonparametric inference. In the Bayesian approach, Dirichlet mixture priors are often used in practice for such problems. However, the asymptotic properties of such priors have only been studied in the...

Missing covariate data are very common in regression analysis. In this paper, the weighted estimating equation method (Qi et al., 2005) [25] is used to extend the so-called unified estimation procedure (Chen et al., 2002) [4] for linear transformation models to the case of missing covariates....

In this paper we propose a new test procedure for sphericity of the covariance matrix when the dimensionality, p, exceeds that of the sample size, N=n+1. Under the assumptions that (A) as p--[infinity] for i=1,...,16 and (B) p/n--c<[infinity] known as the concentration, a new statistic is developed utilizing the ratio of the fourth and second arithmetic means of the eigenvalues of the sample covariance matrix. The newly defined test has many desirable general asymptotic properties, such as normality and consistency when (n,p)-->[infinity]. Our simulation results show that the new test is...</[infinity]>

In this paper, we consider independence models for three-dimensional tables under multinomial sampling. We use the restricted minimum [phi]-divergence estimator in a [phi]-divergence statistic, which is the basis of some new test statistics, for solving the classical problem of testing...

We describe a universal conditional distribution method for uniform sampling from n-spheres and n-balls, based on properties of a family of radially symmetric multivariate distributions. The method provides us with a unifying view on several known algorithms as well as enabling us to construct...

A well-known result in extreme value theory indicates that componentwise taken sample maxima of random vectors are asymptotically independent under weak conditions. However, in important cases this independence is attained at a very slow rate so that the residual dependence structure plays a...

In this paper we study the properties of a kurtosis matrix and propose its eigenvectors as interesting directions to reveal the possible cluster structure of a data set. Under a mixture of elliptical distributions with proportional scatter matrix, it is shown that a subset of the eigenvectors of...

This paper deals with the problem of quantization of a random variable X taking values in a separable and reflexive Banach space, and with the related question of clustering independent random observations distributed as X. To this end, we use a quantization scheme with a class of distortion...

This paper concerns outlier robust non-parametric regression with smoothing splines for data that are possibly correlated. We define a robust smoother as the minimizer of a penalized robustified log likelihood. Our estimation algorithm uses iteratively reweighted least squares to estimate the...