Statistical inference for finite Markov chains based on divergences

DOI10.1016/S0167-7152(98)00106-0zbMath0931.62075OpenAlexW2004174871MaRDI QIDQ1304078

Leandro Pardo, M. Luisa Menendez, Domingo Morales, Konstantinos G. Zografos

Publication date: 11 November 1999

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(98)00106-0

zbMATH Keywords

goodness-of-fit testsminimum distance estimatesdivergence statistics

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05) Markov processes: hypothesis testing (62M02)

Related Items (3)

Robust parametric inference for finite Markov chains ⋮ Csiszar's \(\varphi\)-divergence for testing the order in a Markov chain. ⋮ ASYMPTOTIC PROPERTIES FOR MAXIMUM LIKELIHOOD ESTIMATORS FOR RELIABILITY AND FAILURE RATES OF MARKOV CHAINS

Cites Work

• Goodness-of-fit statistics for discrete multivariate data
• On the applications of divergence type measures in testing statistical hypotheses
• Asymptotic divergence of estimates of discrete distributions
• Estimation by the minimum distance method
• Divergence statistics: sampling properties and multinomial goodness of fit and divergence tests
• On Estimates of the Rate of Convergence in the Central Limit Theorem for Homogeneous Markov Chains
• On the Central Limit Theorem for Markov Chains
• On Asymptotic Behavior of Distributions of Some Statistics for Markov Chains
• Asymptotic distribution of the log-likelihood function for stochastic processes
• The Lindeberg-Levy Theorem for Martingales
• A New Proof of the Pearson-Fisher Theorem
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