Une nouvelle approche de la méthode STATIS
Une nouvelle approche des problèmes de classification automatique
Une nouvelle règle de classement, utilisant l'enveloppe convexe et la mesure de Lebesgue
Une propriété peu connue : l'intervalle de confiance de la médiane
Une remarque sur l'article de M. A. Guldberg: “On discontinuons frequency functions and statistical series.”
Une représentation graphique de la liaison statistique entre deux variables ordonnées
Le stéréogramme de liaison est une représentation graphique simultanée de la distribution conjointe de deux variables ordonnées, de leurs distributions marginales, et de la densité de la première par rapport au produit des deux autres. On y lit une forme de liaison statistique qui est introduite sous le nom de liaison blackienne et dont on discute les rapports avec la liaison stochastique.
Ungünstige Verteilungen bei einparametrigen Parameterschätzproblemen.
Unification de certains critères d'association par linéarisation et normalisation
Uniform asymptotic normality for the Bernoulli scheme
It is easy to notice that no sequence of estimators of the probability of success θ in a Bernoulli scheme can converge (when standardized) to N(0,1) uniformly in θ ∈ ]0,1[. We show that the uniform asymptotic normality can be achieved if we allow the sample size, that is, the number of Bernoulli trials, to be chosen sequentially.
Uniform Confidence Bands for Local Polynomial Quantile Estimators
This paper deals with uniform consistency and uniform confidence bands for the quantile function and its derivatives. We describe a kernel local polynomial estimator of quantile function and give uniform consistency. Furthermore, we derive its maximal deviation limit distribution using an approximation in the spirit of Bickel and Rosenblatt [P.J. Bickel and M. Rosenblatt, Ann. Statist. 1 (1973) 1071–1095].
Uniform convergence of density estimators on spheres
Uniform deterministic equivalent of additive functionals and non-parametric drift estimation for one-dimensional recurrent diffusions
Usually the problem of drift estimation for a diffusion process is considered under the hypothesis of ergodicity. It is less often considered under the hypothesis of null-recurrence, simply because there are fewer limit theorems and existing ones do not apply to the whole null-recurrent class. The aim of this paper is to provide some limit theorems for additive functionals and martingales of a general (ergodic or null) recurrent diffusion which would allow us to have a somewhat unified approach...
Uniform Minimum Variance Unbiased Estimators for Bivariate Families.
Uniform rates of convergence in extreme-value theory. Normal and gamma models
Uniform strong consistency of a frontier estimator using kernel regression on high order moments
We consider the high order moments estimator of the frontier of a random pair, introduced by [S. Girard, A. Guillou and G. Stupfler, J. Multivariate Anal. 116 (2013) 172–189]. In the present paper, we show that this estimator is strongly uniformly consistent on compact sets and its rate of convergence is given when the conditional cumulative distribution function belongs to the Hall class of distribution functions.
Uniformly best linear-quadratic estimator in a special structure of the regression model.
Uniformly Most Accurate Equivariant Prediction Limit.
Unimodal contaminations in testing point null hypothesis.
The problem of testing a point null hypothesis from the Bayesian perspective is considered. The uncertainties are modelled through use of ε?contamination class with the class of contaminations including: i) All unimodal distributions and ii) All unimodal and symmetric distributions. Over these classes, the infimum of the posterior probability of the point null hypothesis is computed and compared with the p?value and a better approach than the one known is obtained.
Uniqueness of the level two Bayesian network representing a probability distribution.
Unit root test in the presence of a single additive outlier small sample case
The one sided unit root test of a first-order autoregressive model in the presence of an additive outlier is considered. In this paper, we present a formula to compute the size and the power of the test when an AO (additive outlier) occurs at a time k. A small sample case is considered only.