Lois asymptotiques des tests et estimateurs de rupture dans un modèle statistique classique
Moderate deviations for two sample t-statistics
Let X1,...,Xn1 be a random sample from a population with mean µ1 and variance , and X1,...,Xn1 be a random sample from another population with mean µ2 and variance independent of {Xi,1 ≤ i ≤ n1}. Consider the two sample t-statistic . This paper shows that ln P(T ≥ x) ~ -x²/2 for any x := x(n1,n2) satisfying x → ∞, x = o(n1 + n2)1/2 as n1,n2 → ∞ provided 0 < c1 ≤ n1/n2 ≤ c2 < ∞. If, in addition, E|X1|3 < ∞, E|Y1|3 < ∞, then holds uniformly in x ∈ (O,o((n1 + n2)1/6))
New estimates and tests of independence in semiparametric copula models
We introduce new estimates and tests of independence in copula models with unknown margins using -divergences and the duality technique. The asymptotic laws of the estimates and the test statistics are established both when the parameter is an interior or a boundary value of the parameter space. Simulation results show that the choice of -divergence has good properties in terms of efficiency-robustness.
On bounds for the asymptotic power and on Pitman efficiencies of the Cramer-von Mises test
On -optimality of the LR tests
On dimensioning of samples in testing hypotheses
On Hodges-Lehmann optimality of LR tests
On second order efficiency of a robust test and approximations of its error probabilities
On some asymptotic results in statistics by way of contiguity
On testing hypotheses approximable by cones
On the Convergence of the Critical Values of Uniformly Most Powerful Unbiased Tests for Two-Sided Hypotheses.
On the power of an asymptotically optimal test for the case of Laplace distribution
In the paper we prove a formula for the limit of the difference between the power of the asymptotically optimal test and the power of the asymptotically most powerful test for the case of Laplace distribution.
On the two-sided multiparameter control
Original title unknown
Permutation tests for multiple changes
Approximations to the critical values for tests for multiple changes in location models are obtained through permutation tests principle. Theoretical results say that the approximations based on the limit distribution and the permutation distribution of the test statistics behave in the same way in the limit. However, the results of simulation study show that the permutation tests behave considerably better than the corresponding tests based on the asymptotic critical value.
Pivot variables, tests and strong consistency.
Propriétés asymptotiques locales des tests
Rate of Convergence to Normality for U-Statistics with Kernel of Arbitrary Degree.
Rupture de modèles : loi asymptotique des statistiques de tests et des estimateurs du maximum de vraisemblance
Rupture de modèles pour des processus de Poisson