Quintela-del-Río (2006) considered the estimation of the maximum hazard under dependence conditions and established strong convergence with rate and asymptotic normality of the estimate. The aim of this paper is to generalize this work to the case of right censored data with covariate. Via a...

The AIC, the multivariate Cp and their modifications have been proposed for multivariate linear regression models under a large-sample framework when the sample size n is large, but the dimension p of the response variables is fixed. In this paper, first we propose a high-dimensional AIC...

We use generalised biplots to develop the important special case of (i) when all variables are categorical and (ii) the samples fall into K recognised groups. We term this Categorical Canonical Variate Analysis (CatCVA), because it has similar characteristics to Rao’s Canonical Variate...

This article studies two regularized robust estimators of scatter matrices proposed (and proved to be well defined) in parallel in Chen et al. (2011) and Pascal et al. (2013), based on Tyler’s robust M-estimator (Tyler, 1987) and on Ledoit and Wolf’s shrinkage covariance matrix estimator...

The effective degrees of freedom is a useful concept for describing model complexity. Recently the number of effective degrees of freedom has been shown to relate to the concept of conditional Akaike information (cAI) in the mixed effects models. This relationship was made explicit under linear...

We give the maximal distance between a copula and itself when the argument is permuted for arbitrary dimension, generalizing a result for dimension two by Nelsen (2007), Klement and Mesiar (2006). Furthermore, we establish a subset of [0,1]d in which this bound might be attained. For each point...

In a generalized linear model of binary data, we consider models based on a general link function including a logistic regression model and a probit model as special cases. For testing the null hypothesis H0 that the considered model is correct, we consider a family of ϕ-divergence...

In this paper we assume that the observed p time series are linear combinations of p latent uncorrelated weakly stationary time series. The problem is then to find an estimate for an unmixing matrix that transforms the observed time series back to uncorrelated time series. The so called SOBI...

The Generalized Inverse Gaussian (GIG) distribution has found many interesting applications; see Jørgensen  [24]. This rich family includes some well-known distributions, such as the inverse Gaussian, gamma and exponential, as special cases. These distributions have been used as the mixing...

Bayesian networks (BNs) have become an essential tool for reasoning under uncertainty in complex models. In particular, the subclass of Gaussian Bayesian networks (GBNs) can be used to model continuous variables with Gaussian distributions. Here we focus on the task of learning GBNs from data....