Markov Chain Order Estimation and Relative Entropy

A. R. Baigorri; C. R. Goncalves; P. A. A. Resende

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arXiv · 0910.0264

Markov Chain Order Estimation and Relative Entropy

A. R. Baigorri·C. R. Goncalves·P. A. A. Resende

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Abstract

We use the $f-divergence$ also called relative entropy as a measure of diversity between probability densities and review its basic properties. In the sequence we define a few objects which capture relevant information from the sample of a Markov Chain to be used in the definition of a couple of estimators i.e. the Local Dependency Level and Global Dependency Level for a Markov chain sample. After exploring their properties we propose a new estimator for the Markov chain order. Finally we show a few tables containing numerical simulation results, comparing the performance of the new estimator with the well known and already established AIC and BIC estimators.

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BibTeX RIS

A. R. Baigorri, C. R. Goncalves, P. A. A. Resende. 2009-10-01. Markov Chain Order Estimation and Relative Entropy. https://arxiv.org/abs/0910.0264

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