Estimation for aggregate models: The aggregate Markov chain

DOI10.2307/3314810zbMath0574.62084OpenAlexW2022435286MaRDI QIDQ3692663

Donald L. McLeish

Publication date: 1984

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3314810

zbMATH Keywords

incomplete dataasymptotic efficiencyestimating equationsweighted least-squares estimatorsmartingale limit theoryaggregate Markov-chain model

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05)

Related Items (9)

Estimation for partially observed Markov processes ⋮ MTD models for aggregate data from higher order Markov chains ⋮ Transform martingale estimating functions ⋮ On inference from Markov chain macro-data using transforms ⋮ Least-squares estimation of transition probabilities from aggregate data ⋮ Quasi Bayesian likelihood ⋮ A minimax approach to consistency and efficiency for estimating equations ⋮ An extension of quasi-likelihood estimation ⋮ The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis.

Cites Work

• Least squares estimation in finite Markov processes
• On the estimation of the parameters of Markov probability models using macro data
• Dependent central limit theorems and invariance principles
• Robust estimation through estimating equations
• Least-squares estimation of transition probabilities from aggregate data
• The information in aggregate data from Markov chains
• An Optimum Property of Regular Maximum Likelihood Estimation
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