Local asymptotic normality and mixed normality for Markov statistical models

DOI10.1007/BF01207516zbMath0685.60016OpenAlexW2041996178MaRDI QIDQ1826192

Jean Jacod, Lucia Ladelli, Reinhard Hoepfner

Publication date: 1990

Published in: Probability Theory and Related Fields (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf01207516

zbMATH Keywords

local asymptotic normalityinfinitesimal generatorstatistical experimentHellinger processestransition kernel for Markov chains

Mathematics Subject Classification ID

Probability distributions: general theory (60E05) Characterization and structure theory of statistical distributions (62E10) Transition functions, generators and resolvents (60J35)

Related Items (19)

Efficient Estimation for Semiparametric Semi-Markov Processes ⋮ Nonparametric estimators for Markov step processes ⋮ Adaptive estimators for parameters of the autoregression function of a Markov chain ⋮ Quasi-likelihood models and optimal inference ⋮ Asymptotic mixed normality and hellinger processes ⋮ Optimality of estimators for misspecified semi-Markov models ⋮ On non-parametric estimation of the Lévy kernel of Markov processes ⋮ Estimation of parameters of linear homogeneous stochastic differential equations ⋮ Quantum estimation via sequential measurements ⋮ Scalable Monte Carlo inference and rescaled local asymptotic normality ⋮ Local asymptotic mixed normality for semimartingale experiments ⋮ On statistics of Markov step processes: Representation of log-likelihood ratio processes in filtered local models ⋮ Information geometry and local asymptotic normality for multi-parameter estimation of quantum Markov dynamics ⋮ Some developments in semiparametric statistics ⋮ Estimators for alternating nonlinear autoregression ⋮ Regularity of models associated with Markov jump processes ⋮ Nonparametric estimation in null recurrent time series. ⋮ Information bounds for Gibbs samplers ⋮ Asymptotic inference for Markov step processes: Observation up to a random time

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