Conditionally gaussian distributions and an application to kalman filtering with stochastic regressors

DOI10.1080/03610928508829086zbMath0595.62096OpenAlexW2093581803MaRDI QIDQ3727192

Heikki Ruskeepaa

Publication date: 1985

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610928508829086

zbMATH Keywords

Kalman filterengineeringeconometricsnormal correlationstochastic regressorsconditionally Gaussian distributionsdeterministic regressorsGaussian discrete-time, dynamic linear modelslagged observations

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Gaussian processes (60G15) Filtering in stochastic control theory (93E11)

Related Items (3)

Generalized dynamic linear models for financial time series ⋮ Systems of seemingly unrelated regression equations with time varying coefficients -- an interplay of Kalman filtering, scoring, EM- and MINQUE-method ⋮ A nonlinear time series model and estimation of missing observations

Cites Work

• Stochastic models, estimation, and control. Vol. 2,3
• Conditional independence for statistical operations
• Some identification and estimation results for regression models with stochastically varying coefficients
• Problems of identification and control
• A Recursive Kalman Filter Forecasting Approach
• Kalman type filter for models with stochastic regressors and applications to econometric models
• Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
• A recursive approach to parameter estimation in regression and time series models
• Linear Statistical Inference and its Applications
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