Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter

DOI10.1016/j.csda.2013.07.038zbMath1471.62011OpenAlexW2021007486MaRDI QIDQ1615200

Kwang Woo Ahn, Kung-Sik Chan

Publication date: 2 November 2018

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2013.07.038

zbMATH Keywords

nonlinear time seriesstate-space modelSIR modelunscented Kalman filter

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10)

Related Items

On the convergence rate of the unscented transformation ⋮ Recursive and iterative least squares parameter estimation algorithms for observability canonical state space systems

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