Let P[eta], [eta] = ([theta], [gamma]) [set membership, variant] [Theta] - [Gamma] [subset of] - k, be a (k + 1)-dimensional exponential family. Let [phi]n*, n [set membership, variant] , be an optimal similar test for the hypothesis {P([theta],[gamma])n: [gamma] [set membership, variant]...

Given a suitable function Fn we define a class of estimators called asymptotic Fn-estimators (i.e., estimators which approximate the solution of Fn([theta]) = 0). It is proved that this class is nonvoid if appropriate regularity conditions are fulfilled and if one has at hand a suitable initial...

It is shown that the probability that a suitably standardized asymptotic maximum likelihood estimator of a vector parameter (i.e., an estimator which approximates the solution of the likelihood equation in a reasonably good way) lies in a measurable convex set can be approximated by an integral...

It is shown that--under appropriate regularity conditions--the conditional distribution of the first p components of a normalized sum of i.i.d. m-dimensional random vectors, given the complementary subvector, admits a Chebyshev-Cramér asymptotic expansion of order o(n-(s-2)/2), uniformly over...

Let [theta](n) denote the maximum likelihood estimator of a vector parameter, based on an i.i.d. sample of size n. The class of estimators [theta](n) + n-1 q([theta](n)), with q running through a class of sufficiently smooth functions, is essentially complete in the following sense: For any...

The asymptotic minimax theorem of LeCam and Hájek is refined by inclusion of terms of order n-1/2. This renders more precise informations about the local properties of superefficient estimator-sequences.

Let ? be a functional defined on a family of probability measures , containing a subfamily . The paper presents a condition involving the gradient of the functional under which--for probability measures in --"adaptation" is possible, i.e., under which the asymptotic variance bound for estimators...