On Sequential Maximum Likelihood Estimation for Exponential Families of Stochastic Processes

DOI10.2307/1403144zbMath0604.62074OpenAlexW2320319456MaRDI QIDQ3740849

Michael Sørensen

Publication date: 1986

Published in: International Statistical Review / Revue Internationale de Statistique (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/1403144

zbMATH Keywords

Laplace transformconsistencyasymptotic normalitystopping timemaximum likelihood estimatorlikelihood ratio test statisticlikelihood functioncounting processesbirth-death processminimal sufficient statisticdiffusion type processBartlett adjustmentsdiscrete and continuous time parameter processesnoncurved exponential family

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05) Stopping times; optimal stopping problems; gambling theory (60G40) Sequential estimation (62L12) Optimal stopping in statistics (62L15)

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Large sample inference for conditional exponential families with applications to nonlinear time series ⋮ Exponential families of stochastic processes and Lévy processes ⋮ Optimum statistical inference from randomly stopped random processes ⋮ Information geometry approach to parameter estimation in Markov chains ⋮ Local asymptotic normality of a sequential model for marked point processes and its applications ⋮ On exponential families of Markov processes ⋮ Minimax sequential estimation plans for exponential-type processes ⋮ Sequential estimation for dependent oberservations with an application to non-standard autoregressive processes ⋮ Conjugate priors for exponential-type processes ⋮ Markov processes and exponential families ⋮ Curved exponential families of stochastic processes and their envelope families ⋮ Conformal Geometry of Statistical Manifold with Application to Sequential Estimation ⋮ Information geometry of reversible Markov chains ⋮ Markov stopping sets and stochastic integrals. Application in sequential estimation for a random diffusion field ⋮ Estimation of the mean of some stationary markov sequences

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