2009/10/01 by Angel Rodolfo Baigorri, A. R. Baigorri, Baigorri, A. R. +6 · 2 citations
Computer Science · Mathematics · #Algorithms and Data Compression #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #FOS: Mathematics #Markov Chains and Monte Carlo Methods #Methodology (stat.ME) #Statistics Theory (math.ST) #math.ST #stat.ME #stat.TH
paper · pdf · doi:10.48550/arxiv.0910.0264
Revised for better and shorter proof, new numerical simulations as well as improved references
openalex publication_date 2009/10/01 · arxiv created 2012/06/19 · arxiv updated 2012/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.