Journal ArticleDOI
Asymptotic Expansions Associated with Some Statistical Estimators in the Smooth Case. 1. Expansions of Random Variables
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This article is published in Theory of Probability and Its Applications.The article was published on 1976-06-01. It has received 15 citations till now. The article focuses on the topics: Asymptotic analysis & Taylor expansions for the moments of functions of random variables.read more
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Journal ArticleDOI
A third-order optimum property of the maximum likelihood estimator
TL;DR: For the class of estimators with bounded, symmetric, and neg-unimodal loss functions, this article showed that for any estimator T(n) there exists q ∗ such that the risk of ∆ + n−1 q ∆ ∆ (θ (n) ) is equal to o(n − 1 2 ) for all loss functions.
Book ChapterDOI
Asymptotic Expansions in Parametric Statistical Theory
TL;DR: A survey of the literature on statistical theory with stochastic expansions can be found in this paper, where the authors explain the possibility of developing a general statistical theory based on asymptotic methods.
Posted Content
Indirect likelihood inference
Michael Creel,Dennis Kristensen +1 more
TL;DR: The maximum indirect likelihood estimator (MIL) estimator as discussed by the authors is a Bayesian estimator that maximizes the likelihood of a set of sample moments in a parametric model, and it has been shown to have a bias reduction property similar to that of the indirect inference estimator.
Higher Order Properties of Bootstrap and Jackknife Bias Corrected Maximum Likelihood Estimators
TL;DR: In this paper, the authors focus on bootstrap and jackknife based bias correction as a way to implement bias corrections in a nonparametric way, and they find that their bias corrected ML estimators have the same higher order variance as the efficient estimator of Pfanzagl and Wefelmeyer.
Journal ArticleDOI
Addendum to “a third-order optimum property of the maximum likelihood estimator”
Johann Pfanzagl,W Wefelmeyer +1 more
TL;DR: In this article, a third-order optimum property of the maximum likelihood estimator is extended to not necessarily symmetric loss functions under an appropriate restriction on the class of competing estimators.