AbstractConsistent, asymptotically efficient and asymptotically normal stepwise estimators are given for a subclass of the uniparametric and multiparametric exponential families. The estimators are derived by using the Robbins-Monro stochastic approximation procedure with certain families of random variables arising from the normalized log-likelihood. Considered in detail are three multivariate normal examples where the maximum likelihood estimators are not tractable
International audienceWe consider parametric exponential families of dimension K on the real line. W...
Nonparametric regression has been particularly well developed. Base on the asymptotic equivalence t...
We investigate penalized maximum log-likelihood estimation for exponential family distributions whos...
Maximum likelihood estimation is a standard approach when confronted with the task of finding estima...
AbstractLet X1,…, Xp be p (≥ 3) independent random variables, where each Xi has a distribution belon...
We consider likelihood-based inference in some continuous exponential families with unknown threshol...
We observe $n$ independent pairs of random variables $(W_{i}, Y_{i})$ for which the conditional dist...
This book presents new findings on nonregular statistical estimation. Unlike other books on this top...
Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule,...
In this article, we investigated estimators with the exponential function for the estimation of the ...
AbstractConsider p independent distributions each belonging to the one parameter exponential family ...
We develop a model by choosing the maximum entropy distribution from the set of models satisfying ce...
This thesis can be divided into two parts. In the first part (Chapter 2) we apply Stein's method in ...
Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule,...
Most results in nonparametric regression theory are developed only for the case of additive noise. I...
International audienceWe consider parametric exponential families of dimension K on the real line. W...
Nonparametric regression has been particularly well developed. Base on the asymptotic equivalence t...
We investigate penalized maximum log-likelihood estimation for exponential family distributions whos...
Maximum likelihood estimation is a standard approach when confronted with the task of finding estima...
AbstractLet X1,…, Xp be p (≥ 3) independent random variables, where each Xi has a distribution belon...
We consider likelihood-based inference in some continuous exponential families with unknown threshol...
We observe $n$ independent pairs of random variables $(W_{i}, Y_{i})$ for which the conditional dist...
This book presents new findings on nonregular statistical estimation. Unlike other books on this top...
Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule,...
In this article, we investigated estimators with the exponential function for the estimation of the ...
AbstractConsider p independent distributions each belonging to the one parameter exponential family ...
We develop a model by choosing the maximum entropy distribution from the set of models satisfying ce...
This thesis can be divided into two parts. In the first part (Chapter 2) we apply Stein's method in ...
Often, sample size is not fixed by design. A key example is a sequential trial with a stopping rule,...
Most results in nonparametric regression theory are developed only for the case of additive noise. I...
International audienceWe consider parametric exponential families of dimension K on the real line. W...
Nonparametric regression has been particularly well developed. Base on the asymptotic equivalence t...
We investigate penalized maximum log-likelihood estimation for exponential family distributions whos...