Asymptotic expansion of the log-likelihood function based on stopping times defined on a Markov process

DOI10.1007/BF02480263zbMath0446.60058MaRDI QIDQ1145951

Michael G. Akritas, George G. Roussas

Publication date: 1979

Published in: Annals of the Institute of Statistical Mathematics (Search for Journal in Brave)

zbMATH Keywords

asymptotic expansionstopping timeslog-likelihood function

Mathematics Subject Classification ID

Discrete-time Markov processes on general state spaces (60J05) Stopping times; optimal stopping problems; gambling theory (60G40) Limit theorems in probability theory (60F99)

Related Items (7)

The contiguity of probability measures and asymptotic inference in continuous time stationary diffusions and Gaussian processes with known covariance ⋮ Estimation of the expected number of earthquake occurrences based on semi-Markov models ⋮ Hájek-Inagaki convolution representation theorem for randomly stopped locally asymptotically mixed normal experiments ⋮ Asymptotic expansion of the log-likelihood function based on stopping times defined on a Markov process ⋮ A note on contiguity and \(L_ 1-\)norm ⋮ Local asymptotic normality for progressively censored likelihood ratio statistics and applications ⋮ On the speed of convergence in the central limit theorem of log- likelihood ratio processes

Cites Work

• Asymptotic expansion of the log-likelihood function based on stopping times defined on a Markov process
• Contiguity of Probability Measures
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