Co-inertie de deux variables aléatoires hilbertiennes et approximation B-spline application en foresterie
Comparaison des méthodes de simulation d'un bruit blanc gaussien
Concomitants and linear estimators in an i-dimensional extremal model.
We consider here a multivariate sample Xj = (X1.j > ... > Xi.j), 1 ≤ j ≤ n, where the Xj, 1 ≤ j ≤ n, are independent i-dimensional extremal vectors with suitable unknown location and scale parameters λ and δ respectively. Being interested in linear estimation of these parameters, we consider the multivariate sample Zj, 1 ≤ j ≤ n, of the order statistic of largest values and their concomitants, and the best linear unbiased estimators of λ and δ based on such multivariate sample. Computational...
Contribuciones a la generalización del problema de compensación por grupos de Helmert-Pranis Pranievich.
The paper presents in a generalized form the problem of the geodetic network adjustment by the Helmert-Pranis Pranievich groups method (groups with junction points included or not). The adjustment problem, as well as the cofactor matrix derivation for the partial-independent and linkage unknowns, was completely formulated by transformed weight matrix definition and usage. A complete sequence of the computing stages for the geodetic networks divided into groups without junction points was given for...
Control of uncertain processes: applied theory and algorithms
Corrector Analysis of a Heterogeneous Multi-scale Scheme for Elliptic Equations with Random Potential
This paper analyzes the random fluctuations obtained by a heterogeneous multi-scale first-order finite element method applied to solve elliptic equations with a random potential. Several multi-scale numerical algorithms have been shown to correctly capture the homogenized limit of solutions of elliptic equations with coefficients modeled as stationary and ergodic random fields. Because theoretical results are available in the continuum setting for such equations, we consider here the case of a second-order...
Criterio para detectar outliers en poblaciones normales bivariantes.
Damos un procedimiento de detección de outliers para muestras procedentes de poblaciones normales bivariantes, que viene dado por el cuadrado de la distancia entre matrices de sumas de cuadrados y sumas de productos de observaciones muestrales, la cual se ha obtenido a partir de la forma métrica diferencial de Maas.