On computing the expected Fisher information matrix for state-space model parameters

DOI10.1016/0167-7152(95)00031-3zbMath0847.62078OpenAlexW1965458961MaRDI QIDQ1916158

Joseph E. Cavanaugh, Robert H. Shumway

Publication date: 8 October 1996

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(95)00031-3

zbMATH Keywords

EM algorithmsimulation resultsKalman filtermaximum likelihood estimatesrecursive algorithmtimes seriesexpected Fisher information matrixstate-space model parameters

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20)

Related Items (9)

Confidence intervals based on the deviance statistic for the hyperparameters in state space models ⋮ Adaptive and robust experimental design for linear dynamical models using Kalman filter ⋮ Computing the Exact Fisher Information Matrix of Periodic State-Space Models ⋮ A NON‐GAUSSIAN FAMILY OF STATE‐SPACE MODELS WITH EXACT MARGINAL LIKELIHOOD ⋮ Testing the hypothesis of absence of unobserved confounding in semiparametric bivariate probit models ⋮ Exact filters for Newton-Raphson parameter estimation algorithms for continuous-time partially observed stochastic systems ⋮ Nonstationary dynamic factor analysis ⋮ Computing the covariance matrix of QML estimators for a state space model ⋮ Approximate state space modelling of unobserved fractional components

Cites Work

• AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM
• NOTE ON THE KALMAN FILTER WITH ESTIMATED PARAMETERS
• Evaluation of likelihood functions for Gaussian signals
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