Consistency of maximum likelihood estimation for some dynamical systems
Abstract
We consider the asymptotic consistency of maximum likelihood parameter estimation for dynamical systems observed with noise. Under suitable conditions on the dynamical systems and the observations, we show that maximum likelihood parameter estimation is consistent. Our proof involves ideas from both information theory and dynamical systems. Furthermore, we show how some well-studied properties of dynamical systems imply the general statistical properties related to maximum likelihood estimation. Finally, we exhibit classical families of dynamical systems for which maximum likelihood estimation is consistent. Examples include shifts of finite type with Gibbs measures and Axiom A attractors with SRB measures.
Cite
@article{arxiv.1306.5603,
title = {Consistency of maximum likelihood estimation for some dynamical systems},
author = {Kevin McGoff and Sayan Mukherjee and Andrew Nobel and Natesh Pillai},
journal= {arXiv preprint arXiv:1306.5603},
year = {2014}
}
Comments
Published in at http://dx.doi.org/10.1214/14-AOS1259 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)