Accepted for publication in Journal of Statistical Planning and InferenceInternational audienceThe aim of this article is to simplify Pfanzagl's proof of consistency for asymptotic maximum likelihood estimators, and to extend it to more general asymptotic M-estimators. The method relies on the existence of a sort of contraction of the parameter space which admits the true parameter as a fixed point. The proofs are short and elementary
Wald's (1949) classical consistency theorem (which proves strong consistency of the maximum likeliho...
Maximum likelihood estimation is one of statistical methods for estimating an unknown parameter. It ...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
We review some first-and higher-order asymptotic techniques for M-estimators and we study their stab...
In M-estimation problems involving estimands in Banach spaces, the M-estimators, when appropriately ...
AbstractM-estimation is a widely used technique for statistical inference. In this paper, we study p...
The asymptotic theory of estimators obtained from estimating functions is re-viewed and some new res...
Consider M-estimation in a semiparametric model that is charac-terized by a Euclidean parameter of i...
AbstractThe strong consistency of M-estimators in linear models is considered. Under some conditions...
AbstractThis paper extends the results of Chen and Wu [1] concerning consistency of M-estimators in ...
Abstract. In the paper we prove strong consistency of estimators as solution of optimisation problem...
. In statistical analyses the complexity of a chosen model is often related to the size of available...
This paper studies the M-estimation in a general conditionally heteroscedastic time series models. S...
AbstractIn statistical analyses the complexity of a chosen model is often related to the size of ava...
The development of the literature on the pseudo maximum likelihood (PML) estimators would not have b...
Wald's (1949) classical consistency theorem (which proves strong consistency of the maximum likeliho...
Maximum likelihood estimation is one of statistical methods for estimating an unknown parameter. It ...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
We review some first-and higher-order asymptotic techniques for M-estimators and we study their stab...
In M-estimation problems involving estimands in Banach spaces, the M-estimators, when appropriately ...
AbstractM-estimation is a widely used technique for statistical inference. In this paper, we study p...
The asymptotic theory of estimators obtained from estimating functions is re-viewed and some new res...
Consider M-estimation in a semiparametric model that is charac-terized by a Euclidean parameter of i...
AbstractThe strong consistency of M-estimators in linear models is considered. Under some conditions...
AbstractThis paper extends the results of Chen and Wu [1] concerning consistency of M-estimators in ...
Abstract. In the paper we prove strong consistency of estimators as solution of optimisation problem...
. In statistical analyses the complexity of a chosen model is often related to the size of available...
This paper studies the M-estimation in a general conditionally heteroscedastic time series models. S...
AbstractIn statistical analyses the complexity of a chosen model is often related to the size of ava...
The development of the literature on the pseudo maximum likelihood (PML) estimators would not have b...
Wald's (1949) classical consistency theorem (which proves strong consistency of the maximum likeliho...
Maximum likelihood estimation is one of statistical methods for estimating an unknown parameter. It ...
AbstractThe asymptotic distribution of multivariate M-estimates is studied. It is shown that, in gen...
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