For inferential analysis of spatial data, probability modelling in the form of a spatial stochastic process is often adopted. In the univariate case, a realization of the process is a surface over the region of interest. The specification of the process has implications for the smoothness of...

This paper is concerned with the null distribution of test statistic T for testing a linear hypothesis in a linear model without assuming normal errors. The test statistic includes typical ANOVA test statistics. It is known that the null distribution of T converges to [chi]2 when the sample size...

The notion of asymptotic efficacy due to Hannan for multivariate statistics in a location problem is reformulated for manifolds. The matrices used in Hannan's definition are reformulated as Riemannian metrics on a manifold and hence are seen not to depend upon the particular parameterization of...

We consider the affine equivariant sign covariance matrix (SCM) introduced by Visuri et al. (J. Statist. Plann. Inference 91 (2000) 557). The population SCM is shown to be proportional to the inverse of the regular covariance matrix. The eigenvectors and standardized eigenvalues of the...

We study the extension of canonical correlation from pairs of random vectors to the case where a data sample consists of pairs of square integrable stochastic processes. Basic questions concerning the definition and existence of functional canonical correlation are addressed and sufficient...

A finite sample performance measure of multivariate location estimators is introduced based on "tail behavior". The tail performance of multivariate "monotone" location estimators and the halfspace depth based "non-monotone" location estimators including the Tukey halfspace median and...

Poisson mixed models are used to analyze a wide variety of cluster count data. These models are commonly developed based on the assumption that the random effects have either the log-normal or the gamma distribution. Obtaining consistent as well as efficient estimates for the parameters involved...

We develop a general class of factor-analytic models for the analysis of multivariate (truncated) count data. Dependencies in multivariate counts are of interest in many applications, but few approaches have been proposed for their analysis. Our model class allows for a variety of distributions...

Recent advances in median regression model have made it possible to use this model for analyzing a variety of censored survival data. For inference on the model parameter vector, there are now semiparametric procedures based on normal approximation that are valid without strong conditions on the...

This paper is intended as an investigation of estimating cause-specific cumulative hazard and cumulative incidence functions in a competing risks model. The proportional model in which ratios of the cause-specific hazards to the overall hazard are assumed to be constant (independent of time) is...