On the Hsu condition in a replicated regression model
On the Jensen-Shannon divergence and the variation distance for categorical probability distributions
We establish a decomposition of the Jensen-Shannon divergence into a linear combination of a scaled Jeffreys' divergence and a reversed Jensen-Shannon divergence. Upper and lower bounds for the Jensen-Shannon divergence are then found in terms of the squared (total) variation distance. The derivations rely upon the Pinsker inequality and the reverse Pinsker inequality. We use these bounds to prove the asymptotic equivalence of the maximum likelihood estimate and minimum Jensen-Shannon divergence...
On the measurement of stochastical dependence
On the moments of random variables uniformly distributed over a polytope.
On the multivariate skew-normal distribution and its scale mixtures.
On the noncentral distribution of the ratio of the extreme roots of the Wishart matrix.
On the optimality of the max-depth and max-rank classifiers for spherical data
The main goal of supervised learning is to construct a function from labeled training data which assigns arbitrary new data points to one of the labels. Classification tasks may be solved by using some measures of data point centrality with respect to the labeled groups considered. Such a measure of centrality is called data depth. In this paper, we investigate conditions under which depth-based classifiers for directional data are optimal. We show that such classifiers are equivalent to the Bayes...
On the order equivalence relation of binary association measures
Over a century of research has resulted in a set of more than a hundred binary association measures. Many of them share similar properties. An overview of binary association measures is presented, focused on their order equivalences. Association measures are grouped according to their relations. Transformations between these measures are shown, both formally and visually. A generalization coefficient is proposed, based on joint probability and marginal probabilities. Combining association measures...
On the principle of equivariance: concepts and applications.
On the properties of the Generalized Normal Distribution
The target of this paper is to provide a critical review and to enlarge the theory related to the Generalized Normal Distributions (GND). This three term (position, scale shape) distribution is based in a strong theoretical background due to Logarithm Sobolev Inequalities. Moreover, the GND is the appropriate one to support the Generalized entropy type Fisher's information measure.
On the robustness of multiple regression coefficient estimators obtained by the p-point method
On the significance level of the multirelation coefficient.
On the Strong Uniform Consistency of a New Kernel Density Estimator.
On the Variance, Reliability and Importance of Canonical Variables
On the Wilks Λ statistic as a tool for model selection in mixed variable discriminant analysis
On two matrix derivatives by Kollo and von Rosen.
The article establishes relationships between the matrix derivatives of F with respect to X as introduced by von Rosen (1988), Kollo and von Rosen (2000) and the Magnus-Neudecker (1999) matrix derivative. The usual transformations apply and the Moore-Penrose inverse of the duplication matrix is used. Both X and F have the same dimension.
On Two Recent Characterizations of Multivariate Normal Distribution.
On uniform tail expansions of bivariate copulas
The theory of copulas provides a useful tool for modelling dependence in risk management. The goal of this paper is to describe the tail behaviour of bivariate copulas and its role in modelling extreme events. We say that a bivariate copula has a uniform lower tail expansion if near the origin it can be approximated by a homogeneous function L(u,v) of degree 1; and it is said to have a uniform upper tail expansion if the associated survival copula has a lower tail expansion. In this paper we (1)...
On uniform tail expansions of multivariate copulas and wide convergence of measures
The theory of copulas provides a useful tool for modeling dependence in risk management. In insurance and finance, as well as in other applications, dependence of extreme events is particularly important, hence there is a need for a detailed study of the tail behaviour of multivariate copulas. We investigate the class of copulas having regular tails with a uniform expansion. We present several equivalent characterizations of uniform tail expansions. Next, basing on them, we determine the class of...
On univariate and bivariate aging for dependent lifetimes with Archimedean survival copulas
Let be a pair of exchangeable lifetimes whose dependence structure is described by an Archimedean survival copula, and let denotes the corresponding pair of residual lifetimes after time , with . This note deals with stochastic comparisons between and : we provide sufficient conditions for their comparison in usual stochastic and lower orthant orders. Some of the results and examples presented here are quite unexpected, since they show that there is not a direct correspondence between univariate...