Maximum likelihood estimates of incorrect Markov models for time series and the derivation of AIC

DOI10.2307/3212924zbMath0444.62098OpenAlexW4249050414MaRDI QIDQ3888396

Yosihiko Ogata

Publication date: 1980

Published in: Journal of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.2307/3212924

zbMATH Keywords

Akaike information criterionmaximum likelihood estimatesMarkov modelsautoregressive modelmaximal correlation coefficientnegative entropyergodic stationary process

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Markov processes: estimation; hidden Markov models (62M05)

Related Items (10)

On a criterion for the selection of models for stationary time series ⋮ ON THE UNBIASEDNESS PROPERTY OF AIC FOR EXACT OR APPROXIMATING LINEAR STOCHASTIC TIME SERIES MODELS ⋮ CROSS-VALIDATORY CHOICE OF A SPECTRUM ESTIMATE AND ITS CONNECTIONS WITH AIC ⋮ Optimality of estimators for misspecified semi-Markov models ⋮ Estimation of interaction potentials of spatial point patterns through the maximum likelihood procedure ⋮ Join-and-Cut algorithm for self-avoiding walks with variable length and free endpoints ⋮ Asymptotically optimal estimation in misspecified time series models ⋮ A special property of the expected log likelihooda special property of the expected log likelihood ⋮ AIC, overfitting principles, and the boundedness of moments of inverse matrices for vector autotregressions and related models. ⋮ ON SOME AMBIGUITIES ASSOCIATED WITH THE FITTING OF ARMA MODELS TO TIME SERIES

This page was built for publication: Maximum likelihood estimates of incorrect Markov models for time series and the derivation of AIC