Revisiting the Neyman-Scott model: an Inconsistent MLE or an Ill-defined Model?
Methodology
2013-01-29 v1
Abstract
The Neyman and Scott (1948) model is widely used to demonstrate a serious weakness of the Maximum Likelihood (ML) method: it can give rise to inconsistent estimators. The primary objective of this paper is to revisit this example with a view to demonstrate that the culprit for the inconsistent estimation is not the ML method but an ill-defined statistical model. It is also shown that a simple recasting of this model renders it well-defined and the ML method gives rise to consistent and asymptotically efficient estimators.
Keywords
Cite
@article{arxiv.1301.6278,
title = {Revisiting the Neyman-Scott model: an Inconsistent MLE or an Ill-defined Model?},
author = {Aris Spanos},
journal= {arXiv preprint arXiv:1301.6278},
year = {2013}
}