Filtering and smoothing algorithms for state space models

DOI10.1016/0898-1221(89)90104-1zbMath0694.62044OpenAlexW2054807700MaRDI QIDQ909400

Robert Kohn, Craig F. Ansley

Publication date: 1989

Published in: Computers \& Mathematics with Applications (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0898-1221(89)90104-1

zbMATH Keywords

smoothing algorithmsfiltering algorithmsstate modellimiting normalized likelihoodmodified Kalman filterpartially diffuse initial conditions

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20)

Cites Work

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• Estimation, filtering, and smoothing in state space models with incompletely specified initial conditions
• Covariance matrix computation of the state variable of a stationary Gaussian process
• Fixed interval estimation in state space models when some of the data are missing or aggregated
• Estimation, Prediction, and Interpolation for ARIMA Models with Missing Data
• Efficient generalized cross-validation for state space models
• A New Algorithm for Spline Smoothing Based on Smoothing a Stochastic Process
• Signal extraction for finite nonstationary time series
• A cross-validation filter for time series models
• A fast algorithm for signal extraction, influence and cross-validation in state space models
• Maximum Likelihood Fitting of ARMA Models to Time Series with Missing Observations
• Evaluation of likelihood functions for Gaussian signals

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