Estimation and tests of hypotheses for the initial mean and covariance in the kalman filter model

DOI10.1080/03610928108828137zbMath0472.62095OpenAlexW1991187469MaRDI QIDQ3925754

L. J. Levy, Dale E. Olsen, Robert H. Shumway

Publication date: 1981

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

Full work available at URL: https://doi.org/10.1080/03610928108828137

zbMATH Keywords

consistencyasymptotic normalitytime seriesmeanlinear modelstestscovariancesiterative maximum likelihood

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric hypothesis testing (62F03) Point estimation (62F10)

Related Items

Maximum likelihood mean and covariance matrix estimation constrained to general positive semi-definiteness, SOME DOUBLY STOCHASTIC TIME SERIES MODELS, First-order data sensitivity measures with applications to a multivariate signal-plus-noise problem, Effect of imprecisely known nuisance parameters on estimates of primary parameters, Nonparametric EM algorithms for estimating prior distributions, Efficient Monte Carlo computation of Fisher information matrix using prior information, AN APPROACH TO TIME SERIES SMOOTHING AND FORECASTING USING THE EM ALGORITHM, An approximation for analyzing a broad class of implicitly and explicitly defined estimators, Cyclic seesaw process for optimization and identification, An implicit function based procedure for analyzing maximum likelihood estimates from nonidentically distributed data

Cites Work

• Unnamed Item
• Unnamed Item
• Asymptotically efficient estimation of covariance matrices with linear structure
• Stochastic processes and filtering theory
• The asymptotic properties of ML estimators when sampling from associated populations
• Computational aspects of maximum likelihood estimation and reduction in sensitivity function calculations
• Newton-Raphson and Related Algorithms for Maximum Likelihood Variance Component Estimation
• A recursive approach to parameter estimation in regression and time series models
• Linear system identification from nonstationary cross-sectional data
• Maximum-likelihood estimation for the mixed analysis of variance model
• Asymptotic Properties of Maximum Likelihood Estimators for the Independent Not Identically Distributed Case
• A NOTE ON THE CONSISTENCY AND MAXIMA OF THE ROOTS OF LIKELIHOOD EQUATIONS