Minimum Variance Unbiased Estimation of Left Truncated Multivariate Modified Power Series Distribution.
Minimum Variance Unbiased Estimation of Multivariate Modified Power Series Distribution.
Misclassified multinomial data: a Bayesian approach.
In this paper, the problem of inference with misclassified multinomial data is addressed. Over the last years there has been a significant upsurge of interest in the development of Bayesian methods to make inferences with misclassified data. The wide range of applications for several sampling schemes and the importance of including initial information make Bayesian analysis an essential tool to be used in this context. A review of the existing literature followed by a methodological discussion is...
Mixed multiepoch linear regression models with nuisance parameters
Mixturas de distribuciones normales con aplicaciones a problemas estadísticos complejos.
Model and method for analysis of categorical data with relations [Abstract of thesis]
Model selection for estimating the non zero components of a Gaussian vector
We propose a method based on a penalised likelihood criterion, for estimating the number on non-zero components of the mean of a Gaussian vector. Following the work of Birgé and Massart in Gaussian model selection, we choose the penalty function such that the resulting estimator minimises the Kullback risk.
Modèle linéaire généralisé et analyse des correspondances
Modèle log-linéaire et modèle de Cox dans l'analyse d'une table de contingence
Modèles d'optimisation en analyse des données relationnelles
Modeling biased information seeking with second order probability distributions
Updating probabilities by information from only one hypothesis and thereby ignoring alternative hypotheses, is not only biased but leads to progressively imprecise conclusions. In psychology this phenomenon was studied in experiments with the “pseudodiagnosticity task”. In probability logic the phenomenon that additional premises increase the imprecision of a conclusion is known as “degradation”. The present contribution investigates degradation in the context of second order probability distributions....
Modeling conditional and marginal association in contingency tables
Modelling financial time series using reflections of copulas
We have intensified studies of reflections of copulas (that we introduced recently in [6]) and found that their convex combinations exhibit potentially useful fitting properties for original copulas of the Normal, Frank, Clayton and Gumbel types. We show that these properties enable us to construct interesting models for the relations between investment in stocks and gold.
Models gràfics d'independència.
Los modelos gráficos de independencia son una herramienta del análisis multivariante que utiliza gráficos para representar modelos. En particular, los grafos de independencia resumen y clarifican las interacciones entre variables, interacciones no siempre fáciles de interpretar, especialmente cuando en ellas intervienen tres o más variables.En este trabajo se proporciona, en clave pedagógica, una introducción a la teoría de grafos de independencia, comenzando por las nociones de independencia necesarias...
Modified power divergence estimators in normal models – simulation and comparative study
Point estimators based on minimization of information-theoretic divergences between empirical and hypothetical distribution induce a problem when working with continuous families which are measure-theoretically orthogonal with the family of empirical distributions. In this case, the -divergence is always equal to its upper bound, and the minimum -divergence estimates are trivial. Broniatowski and Vajda [3] proposed several modifications of the minimum divergence rule to provide a solution to the...
Monotonicity of the Power Function and Unbiasedness of Some Likelihood Ratio Tests.
Monotonicity properties of statistical procedures for an increasing number of observations or a decreasing number of parameters, especially Anova procedures
MSE-Improvement of the Least Squares Estimator by Dropping Variables.
M-Tests in Multivariate Models.
Multi-label classification using error correcting output codes
A framework for multi-label classification extended by Error Correcting Output Codes (ECOCs) is introduced and empirically examined in the article. The solution assumes the base multi-label classifiers to be a noisy channel and applies ECOCs in order to recover the classification errors made by individual classifiers. The framework was examined through exhaustive studies over combinations of three distinct classification algorithms and four ECOC methods employed in the multi-label classification...