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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...
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.
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.
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...
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...
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...
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...
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...
A model following control system (MFCS) can output general signals following the desired ones. In this paper, a method of nonlinear MFCS will be extended to be a nonlinear descriptor system in discrete time. The nonlinear system studied in this paper has the property of norm constraint , where , , . In this case, a new criterion is proposed to ensure the internal states be stable.
En este trabajo consideramos el problema de la detección de rasgos bajo la presencia de ruido en imágenes que tras un cierto tratamiento se reducen a binarias, por la presencia de dos tipos de elementos. Podemos encontrar ejemplos de este problema en la detección de minas por medio de imágenes de avión o satélite, en la búsqueda de rasgos en imágenes microscópicas de células, o en la caracterización de fallas en zonas de terremotos.En primer lugar revisamos algunos métodos de detección jerárquicos...
Detecting a cluster structure is considered. This means solving either the problem of discovering a natural decomposition of data points into groups (clusters) or the problem of detecting clouds of data points of a specific form. In this paper both these problems are considered. To discover a cluster structure of a specific arrangement or a cloud of data of a specific form a class of nonlinear projections is introduced. Fitness functions that estimate to what extent a given subset of data points...
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