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We review recent results about the maximal values of the Kullback-Leibler information divergence from statistical models defined by neural networks, including naive Bayes models, restricted Boltzmann ...
In this paper we study how perturbing a matrix changes its non-negative rank. We show that the non-negative rank is upper-semicontinuos and we describe some special families of perturbations. We apply...
Statistical models defined by sufficiency。
A large number of statistical models is described by a family of reversed submartingales converging to degenerated limits. The problem under consideration is to estimate the maximum points of the l...
The paper outlines some aspects related to statistical model selection, focusing in particular on inference conducted in the presence of a finite set of parametric models. The point the paper emphasiz...
The aim of this work is to propose models to study the toxic effect of different concentrations of some standard mutagens in different colon cancer cell lines. We find estimates and, by means of an in...
Many statistical models are algebraic in that they are defined in terms of polynomial constraints, or in terms of polynomial or rational parametrizations. The parameter spaces of such models are typ...
This paper describes a new approach to time series modeling that combines subject-matter knowledge of the system dynamics with statistical techniques in time series analysis and regression. Applicat...
We study two statistical models for short-length categorical (or ordinal) time series. The first one is a regression model based on generalized linear model. The second one is a parametrized Markovia...

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