Journal article
Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter
Computational statistics & data analysis, Vol.69, pp.243-254
01/2014
DOI: 10.1016/j.csda.2013.07.038
Abstract
The problem of estimating a nonlinear state-space model whose state process is driven by an ordinary differential equation (ODE) or a stochastic differential equation (SDE), with discrete-time data is studied. A new estimation method is proposed based on minimizing the conditional least squares (CLS) with the conditional mean function computed approximately via the unscented Kalman filter (UKF). Conditions are derived for the UKF–CLS estimator to preserve the limiting properties of the exact CLS estimator, namely, consistency and asymptotic normality, under the framework of infill asymptotics, i.e. sampling is increasingly dense over a fixed domain. The efficacy of the proposed method is demonstrated by simulation and a real application.
Details
- Title: Subtitle
- Approximate conditional least squares estimation of a nonlinear state-space model via an unscented Kalman filter
- Creators
- Kwang Woo Ahn - Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI 53226, United StatesKung-Sik Chan - Department of Statistics and Actuarial Science, The University of Iowa, Iowa City, IA 52242, United States
- Resource Type
- Journal article
- Publication Details
- Computational statistics & data analysis, Vol.69, pp.243-254
- DOI
- 10.1016/j.csda.2013.07.038
- ISSN
- 0167-9473
- eISSN
- 1872-7352
- Publisher
- Elsevier B.V
- Language
- English
- Date published
- 01/2014
- Academic Unit
- Statistics and Actuarial Science; Radiology
- Record Identifier
- 9983985992102771
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