FILTERING AND SMOOTHING IN STATE SPACE MODELS WITH PARTIALLY DIFFUSE INITIAL CONDITIONS

DOI10.1111/j.1467-9892.1990.tb00058.xzbMath0716.62096OpenAlexW2085857803MaRDI QIDQ3203895

Robert Kohn, Craig F. Ansley

Publication date: 1990

Published in: Journal of Time Series Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9892.1990.tb00058.x

zbMATH Keywords

likelihoodKalman filtermarginal likelihoodsmoothing algorithmsignal extractionfiltering algorithmARIMA modelstate space models with partially diffuse initial conditions

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Probabilistic methods, stochastic differential equations (65C99)

Related Items

A Bayesian approach to model selection in stochastic coefficient regression models and structural time series models, Reml and best linear unbiased prediction in state space models, Accuracy and efficiency of alternative spline smoothing algorithms, Minimally conditioned likelihood for a nonstationary state space model, Recursive estimation in econometrics, Signal extraction and filtering by linear semiparametric methods, Likelihood functions for state space models with diffuse initial conditions, Semiparametric approaches to signal extraction problems in economic time series, Extensions to the invariance property of maximum likelihood estimation for affine‐transformed state‐space models, A Bayesian approach to additive semiparametric regression, Diffuse Restricted Kalman Filtering

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