Estimation automatique de la densité
Estimation non paramétrique du -quantile conditionnel et ses dérivées partielles
Estimation non-paramétrique par noyaux : régression polynomiale mobile
Experience in computing optimal regression by branch and bound
Finding an ε-optimal regression subset
Fractional Brownian motion and the Markov property.
Generalized method of least squares collocation
Two general solutions of the collocation problem of physical geodesy are given. Their mutual equivalency and equivalency of them to the classical solution in the regular case are proved. The regularity means the non-singularity of the covariance matrix of those random variables by outcomes of which the measured values of the gravitational field are generated.
Geometry of the nonlinear regression with prior.
Intractability results in discrete-event simulation
Iterative Methods for Overflow Queuing Models I.
Iterative Methods for Overflow Queuing Models II.
L'algorithme SEM : un algorithme d'apprentissage probabiliste pour la reconnaissance de mélange de densités
L'inverse d'une matrice d'intervalles
Maximal association for the sum of squares of a contingency table
Method of Ritz for random eigenvalue problems
Modèles d'optimisation en analyse des données relationnelles
Numerical study of the 6-vertex model with domain wall boundary conditions
A Markov process converging to a random state of the 6-vertex model is constructed. It is used to show that a droplet of c-vertices is created in the antiferromagnetic phase and that the shape of this droplet has four cusps.
On a simulation of the oscillation excited by a random force
On almost sure convergence of modified Euler-Peano approximation of solution to an S.D.E. driven by a semimartingale
On equivalence problem in linear regression models. I. BLUE of the mean value
There exist many different ways of determining the best linear unbiased estimation of regression coefficients in general regression model. In Part I of this article it is shown that all these ways are numerically equivalent almost everyvhere. In Part II conditions are considered under which all the unbiased estimations of the unknown covariance matrix scalar factor are numerically equivalent almost everywhere.