Estimation of AR and ARMA models by stochastic complexity

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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.
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)

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Statistics Theory, 62B10 (Primary) 91B70 (Secondary)

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http://salesiana.dossiersoluciones.com/handle/123456789/131378

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/math/0702765
 http://arxiv.org/abs/math/0702765
 IMS Lecture Notes Monograph Series 2006, Vol. 52, 48-59
 doi:10.1214/074921706000000941

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arXiv