Décomposition des interactions dans une correspondance multiple
Décomposition des processus aléatoires, une comparaison entre les méthodes factorielles et l'analyse spectrale
Defects and transformations of quasi-copulas
Six different functions measuring the defect of a quasi-copula, i. e., how far away it is from a copula, are discussed. This is done by means of extremal non-positive volumes of specific rectangles (in a way that a zero defect characterizes copulas). Based on these defect functions, six transformations of quasi-copulas are investigated which give rise to six different partitions of the set of all quasi-copulas. For each of these partitions, each equivalence class contains exactly one copula being...
Deficiencias del test de la razón de verosimilitud para contrastar ciertas hipótesis con restricciones de orden.
En este trabajo se estudian problemas de test de hipótesis para el vector de las medidas de poblaciones normales independientes con varianzas conocidas, cuando ambas, la hipótesis nula y la alternativa, imponen restricciones de orden sobre los parámetros.Se demuestra que para las hipótesis planteadas el test de razón de verosimilitud (TRV) está dominado por otro TRV para hipótesis que desprecian parte de la información de que se dispone.
Degenerate limit laws for the maxima of trigonometric polynomials with random coefficients
Démonstrations élémentaires des lois fondamentales de probabilité des écarts dans les méthodes expérimentales
Denoising Manifolds for Dimension
2000 Mathematics Subject Classification: 68T01, 62H30, 32C09.Locally Linear Embedding (LLE) has gained prominence as a tool in unsupervised non-linear dimensional reduction. While the algorithm aims to preserve certain proximity relations between the observed points, this may not always be desirable if the shape in higher dimensions that we are trying to capture is observed with noise. This note suggests that a desirable first step is to remove or at least reduce the noise in the observations before...
Densities of determinant ratios, their moments and some simultaneous confidence intervals in the multivariate Gauss-Markoff model
The following three results for the general multivariate Gauss-Markoff model with a singular covariance matrix are given or indicated. determinant ratios as products of independent chi-square distributions, moments for the determinants and the method of obtaining approximate densities of the determinants.
Density Estimates of Discriminant Functions
Density estimation via best -approximation on classes of step functions
We establish consistent estimators of jump positions and jump altitudes of a multi-level step function that is the best -approximation of a probability density function . If itself is a step-function the number of jumps may be unknown.
Density estimation with quadratic loss: a confidence intervals method
We propose a feature selection method for density estimation with quadratic loss. This method relies on the study of unidimensional approximation models and on the definition of confidence regions for the density thanks to these models. It is quite general and includes cases of interest like detection of relevant wavelets coefficients or selection of support vectors in SVM. In the general case, we prove that every selected feature actually improves the performance of the estimator. In the case...
Density smoothness estimation problem using a wavelet approach
In this paper we consider a smoothness parameter estimation problem for a density function. The smoothness parameter of a function is defined in terms of Besov spaces. This paper is an extension of recent results (K. Dziedziul, M. Kucharska, B. Wolnik, Estimation of the smoothness parameter). The construction of the estimator is based on wavelets coefficients. Although we believe that the effective estimation of the smoothness parameter is impossible in general case, we can show that it becomes...
Departure of Bartlett's Distribution of the Homogeneity of Variances for Unequal Sample Sizes from that of Equal Sample Sizes.
Dependence Measuring from Conditional Variances
A conditional variance is an indicator of the level of independence between two random variables. We exploit this intuitive relationship and define a measure v which is almost a measure of mutual complete dependence. Unsurprisingly, the measure attains its minimum value for many pairs of non-independent ran- dom variables. Adjusting the measure so as to make it invariant under all Borel measurable injective trans- formations, we obtain a copula-based measure of dependence v* satisfying A. Rényi’s...
Dependence of Stock Returns in Bull and Bear Markets
Despite of its many shortcomings, Pearson’s rho is often used as an association measure for stock returns. A conditional version of Spearman’s rho is suggested as an alternative measure of association. This approach is purely nonparametric and avoids any kind of model misspecification. We derive hypothesis tests for the conditional rank-correlation coefficients particularly arising in bull and bear markets and study their finite-sample performance by Monte Carlo simulation. Further, the daily returns...
Dependent Gaussian samples: estimation of the scatter uniform in dimension.
Dependent Lindeberg central limit theorem and some applications
In this paper, a very useful lemma (in two versions) is proved: it simplifies notably the essential step to establish a Lindeberg central limit theorem for dependent processes. Then, applying this lemma to weakly dependent processes introduced in Doukhan and Louhichi (1999), a new central limit theorem is obtained for sample mean or kernel density estimator. Moreover, by using the subsampling, extensions under weaker assumptions of these central limit theorems are provided. All the usual causal...
Dependent random variables with a given marginal distribution
Déploiement d'une matrice de corrélation sur la sphère unité de R 3
Der diskontierte Einarmige Bandit.