A recursive approach to parameter estimation in regression and time series models

DOI10.1080/03610927908827825zbMath0408.62074OpenAlexW2049695461MaRDI QIDQ4195810

Johannes Ledolter

Publication date: 1979

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

Full work available at URL: https://doi.org/10.1080/03610927908827825

zbMATH Keywords

RegressionExtended Kalman FilterRecursive EstimationArima Time Series ModelsTime Varying Parameters

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Linear inference, regression (62J99) System identification (93B30) Estimation and detection in stochastic control theory (93E10) Inference from stochastic processes (62M99)

Related Items (6)

Conditionally gaussian distributions and an application to kalman filtering with stochastic regressors ⋮ A Bayesian approach to time-varying cross-sectional regression models ⋮ AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM ⋮ Recursive parameter estimation of transfer function models ⋮ Semiparametric Residuals and Analysis for a Scleroderma Clinical Trial ⋮ Estimation and tests of hypotheses for the initial mean and covariance in the kalman filter model

Cites Work

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• The first-order moving average process. Identification, estimation and prediction
• Stochastic processes and filtering theory
• An Algorithm for Least-Squares Estimation of Nonlinear Parameters
• The exact likelihood function for a mixed autoregressive-moving average process
• Computation of the exact likelihood function of an arima process
• SOME THEOREMS IN LEAST SQUARES

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