Estimation of AR and ARMA models by stochastic complexity

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dc.creatorGiurcăneanu, Ciprian Doru
dc.creatorRissanen, Jorma
dc.date2007-02-26
dc.date.accessioned2026-07-07T08:08:46Z
dc.date.available2026-07-07T08:08:46Z
dc.descriptionIn 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.
dc.descriptionPublished 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)
dc.identifierhttps://arxiv.org/abs/math/0702765
dc.identifierhttp://arxiv.org/abs/math/0702765
dc.identifierIMS Lecture Notes Monograph Series 2006, Vol. 52, 48-59
dc.identifierdoi:10.1214/074921706000000941
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/131378
dc.subjectStatistics Theory
dc.subject62B10 (Primary) 91B70 (Secondary)
dc.titleEstimation of AR and ARMA models by stochastic complexity
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