Notes on stochastic approximation methods
Notes on the bias of dissimilarity indices for incomplete data sets: the case of archaelogical classification.
The problem of missing data is particularly present in archaeological research where, because of the fragmentariness of the finds, only a part of the characteristics of the whole object can be observed. The performance of various dissimilarity indices differently weighting missing values is studied on archaeological data via a simulation. An alternative solution consisting in randomly substituting missing values with character sets is also examined. Gower's dissimilarity coefficient seems to be...
Notes on the evolution of feature selection methodology
The paper gives an overview of feature selection techniques in statistical pattern recognition with particular emphasis on methods developed within the Institute of Information Theory and Automation research team throughout recent years. Besides discussing the advances in methodology since times of Perez’s pioneering work the paper attempts to put the methods into a taxonomical framework. The methods discussed include the latest variants of the optimal algorithms, enhanced sub-optimal techniques...
Notion of information and independent component analysis
Partial orderings and measures of information for continuous univariate random variables with special roles of Gaussian and uniform distributions are discussed. The information measures and measures of non-Gaussianity including the third and fourth cumulants are generally used as projection indices in the projection pursuit approach for the independent component analysis. The connections between information, non-Gaussianity and statistical independence in the context of independent component analysis...
Nouveau critère de segmentation pour des variables à expliquer qualitative ordinale et quantitative multidimensionnelle
Nuclei segmentation for computer-aided diagnosis of breast cancer
Nuevas medidas de información paramétricas reales basadas en la matriz de Fisher.
Se proponen en este trabajo nuevos funcionales reales de la matriz de información de Fisher como medidas de información paramétricas. Se analizan las propiedades de dichas medidas. Se presenta un método sencillo, basado en la matriz de Fisher, para obtener medidas de información paramétricas reales con la propiedad de invariancia bajo transformaciones biyectivas del espacio paramétrico.
Nuevos modelos de distribuciones de extremos basados en aproximaciones en las ramas.
En este trabajo se presenta una metodología que permite clasificar funciones de distribución absolutamente continuas unidimensionales atendiendo a sus ramas. La idea básica es que, en las ramas la función de distribución difiere en un infinitésimo del valor uno o cero dependiendo de la rama de interés. La principal ventaja de esta clasificación es su aplicación a la teoría de distribuciones de extremos. En esta línea se obtienen nuevas familias de distribuciones de extremos. Entre ellas, las clásicas...
Nuisance parameters in financial statistics
Null distribution of multiple correlation coefficient under mixture normal model.
Number of hidden states and memory: a joint order estimation problem for Markov chains with Markov regime
This paper deals with order identification for Markov chains with Markov regime (MCMR) in the context of finite alphabets. We define the joint order of a MCMR process in terms of the number k of states of the hidden Markov chain and the memory m of the conditional Markov chain. We study the properties of penalized maximum likelihood estimators for the unknown order (k, m) of an observed MCMR process, relying on information theoretic arguments. The novelty of our work relies in the joint...
Numerical experiments with least squares spline estimators in a parametric regression model.
Numerical methods for linear minimax estimation
We discuss two numerical approaches to linear minimax estimation in linear models under ellipsoidal parameter restrictions. The first attacks the problem directly, by minimizing the maximum risk among the estimators. The second method is based on the duality between minimax and Bayes estimation, and aims at finding a least favorable prior distribution.
Numerical realization of the Bayesian inversion accelerated using surrogate models
The Bayesian inversion is a natural approach to the solution of inverse problems based on uncertain observed data. The result of such an inverse problem is the posterior distribution of unknown parameters. This paper deals with the numerical realization of the Bayesian inversion focusing on problems governed by computationally expensive forward models such as numerical solutions of partial differential equations. Samples from the posterior distribution are generated using the Markov chain Monte...
Numerical taxonomy: a missing link for case-based reasoning and autonomous agents.
Numerical taxonomy, which uses numerical methods to classify and relate items whose properties are non-numerical, is suggested as both an advantageous tool to support case-based reasoning and a means for agents to exploit knowledge that is best expressed in cases. The basic features of numerical taxonomy are explained, and discussed in application to a problem where human agents with differing views obtain solutions by negotiation and by reference to knowledge that is essentially case-like: allocation...