Estimation of AR and ARMA models by stochastic complexity
Statistics Theory
2007-06-13 v1 Statistics Theory
Abstract
In this paper the stochastic complexity criterion is applied to estimation of the order in AR and ARMA models. The power of the criterion for short strings is illustrated by simulations. It requires an integral of the square root of Fisher information, which is done by Monte Carlo technique. The stochastic complexity, which is the negative logarithm of the Normalized Maximum Likelihood universal density function, is given. Also, exact asymptotic formulas for the Fisher information matrix are derived.
Cite
@article{arxiv.math/0702765,
title = {Estimation of AR and ARMA models by stochastic complexity},
author = {Ciprian Doru Giurcăneanu and Jorma Rissanen},
journal= {arXiv preprint arXiv:math/0702765},
year = {2007}
}
Comments
Published at http://dx.doi.org/10.1214/074921706000000941 in the IMS Lecture Notes Monograph Series (http://www.imstat.org/publications/lecnotes.htm) by the Institute of Mathematical Statistics (http://www.imstat.org)