In many areas of application, the data are of functional nature, such as (one-dimensional) spectral data and two- or three-dimensional imaging data. It is often of interest to test for the significance of some set of factors in the functional observations (e.g.,test for the mean differences...

Motivated by a practical problem, [Z.W. Cai, P.A. Naik, C.L. Tsai, De-noised least squares estimators: An application to estimating advertising effectiveness, Statist. Sinica 10 (2000) 1231-1243] proposed a new regression model with noised variables due to measurement errors. In this model, the...

Multivariate extreme value distribution functions (EVDs) with standard reverse exponential margins and the pertaining multivariate generalized Pareto distribution functions (GPDs) can be parametrized in terms of their Pickands dependence function D with D=1 representing tail independence....

In this paper, we consider sequences of vector martingale differences of increasing dimension. We show that the Kantorovich distance from the distribution of the k(n)-dimensional average of n martingale differences to the corresponding Gaussian distribution satisfies certain inequalities. As a...

This work aims to predict exponentials of mixed effects under a multivariate linear regression model with one random factor. Such quantities are of particular interest in prediction problems where the dependent variable is the logarithm of the variable that is the object of inference....

Let be a response variable that is subject to left-truncation by a variable . We consider the problem of estimating its conditional quantile function given a vector of covariates . We derive almost sure (a.s.) consistency and asymptotic normality results for a kernel estimate of the conditional...

We introduce a class of absolutely continuous bivariate exponential distributions, generated from quadratic forms of standard multivariate normal variates. This class is quite flexible and tractable, since it is regulated by two parameters only, derived from the matrices of the quadratic forms:...

In this paper, we consider a semiparametric regression model where the unknown regression function is the sum of parametric and nonparametric parts. The parametric part is a finite-dimensional multiple regression function whereas the nonparametric part is represented by an infinite series of...

A decomposition of the independence empirical copula process into a finite number of asymptotically independent sub-processes was studied by Deheuvels. Starting from this decomposition, Genest and Rmillard recently investigated tests of independence among random variables based on Cramr-von...

Let denote a set of n independent identically distributed k-dimensional absolutely continuous random variables. A general class of complete orderings of such random vectors is supplied by viewing them as concomitants of an auxiliary random variable. The resulting definitions of multivariate...