Un algorithme de calcul de la probabilité qu'une variable de Fisher-Snedecor, à premier degré de liberté impair, excède une valeur donnée
Un algoritmo iterativo para la estimación de modelos ARMA con ausencia de observaciones.
En el presente artículo se muestra un algoritmo iterativo para la estimación de parámetros de modelos ARMA en series temporales que tengan alguna observación ausente. Posteriormente se efectúa la demostración de la convergencia de dicho algoritmo. Se presenta un ejemplo de estimación basado en la simulación de series temporales con un ordenador y se exponen las conclusiones llevadas a cabo por el autor.
Un calcul probabiliste en génie civil. Évaluation de la probabilité de ruine des structures par une méthode de Monte-Carlo fondée sur une technique de conditionnement
Un método para la simulación de funciones aleatorias estacionarias de segundo orden.
A method is shown for the simulation in Rn of second order stationary random functions with given isotropic covariance. Particular solutions, ilustrated with examples, are provided in R1, R2 and R3.
Uncertainty of the design and covariance matrices in linear statistical model
The aim of the paper is to determine an influence of uncertainties in design and covariance matrices on estimators in linear regression model.
Underparametrization of weakly nonlinear regression models
A large number of parameters in regression models can be serious obstacle for processing and interpretation of experimental data. One way how to overcome it is an elimination of some parameters. In some cases it need not deteriorate statistical properties of estimators of useful parameters and can help to interpret them. The problem is to find conditions which enable us to decide whether such favourable situation occurs.
Une méthode de discrétisation de variables continues
Uniformly convergent adaptive methods for a class of parametric operator equations∗
We derive and analyze adaptive solvers for boundary value problems in which the differential operator depends affinely on a sequence of parameters. These methods converge uniformly in the parameters and provide an upper bound for the maximal error. Numerical computations indicate that they are more efficient than similar methods that control the error in a mean square sense.
Uniformly convergent adaptive methods for a class of parametric operator equations∗
We derive and analyze adaptive solvers for boundary value problems in which the differential operator depends affinely on a sequence of parameters. These methods converge uniformly in the parameters and provide an upper bound for the maximal error. Numerical computations indicate that they are more efficient than similar methods that control the error in a mean square sense.
Uniqueness of the level two Bayesian network representing a probability distribution.
Unit root testing in the presence of innovation variance breaks: a simple solution with increased power.
Using Inside-Outside Algorithm for Estimation of the Offspring Distribution in Multitype Branching Processes
Multitype branching processes (MTBP) model branching structures, where the nodes of the resulting tree are particles of different types. Usually such a process is not observable in the sense of the whole tree, but only as the “generation” at a given moment in time, which consists of the number of particles of every type. This requires an EM-type algorithm to obtain a maximum likelihood (ML) estimate of the parameters of the branching process. Using a version of the inside-outside algorithm for stochastic context-free...
Using Monte Carlo Methods to Evaluate Sub-Optimal Exercise Policies for American Options
∗This research, which was funded by a grant from the Natural Sciences and Engineering Research Council of Canada, formed part of G.A.’s Ph.D. thesis [1].In this paper we use a Monte Carlo scheme to find the returns that an uninformed investor might expect from an American option if he followed one of several näıve exercise strategies rather than the optimal exercise strategy. We consider several such strategies that an ill-advised investor might follow. We also consider how the expected return...
Uso del estadístico Dn de Kolmogorov-Smirnov en inferencia paramétrica.
Se estudia un método de estimación paramétrica basado en la minimización del estadístico Dn de Kolmogorov-Smirnov. Se prueba la existencia y unicidad de este estimador en familias de distribuciones monótonas en alguno de sus parámetros y se compara computacionalmente con el método de máxima verosimilitud.