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Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space models

Conference Paper


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Abstract


  • This paper considers parameters estimation for nonlinear and non-Gaussian state-space models with correlation. We propose an asymptotic quasi-likelihood (AQL) approach which utilises a nonparameteric kernel estimator of conditional variance covariance matrix \Sigma_t to replace the true \Sigma_t in the standard quasi-likelihood. The Kernel estimation avoids the risk of potential miss-specification of \Sigma_t and thus makes the parameter estimator more robust. This has been further verified by empirical studies carried out in this paper.

Authors


Publication Date


  • 2007

Citation


  • Alzghool, R. & Lin, Y. (2007). Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space models. The 2007 International Conference of Computational Statistics and Data Engineering. World Congress on Engineering (pp. 926-932). London: Newswood Limited, International Association of Engineers.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=10350&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3014

Start Page


  • 926

End Page


  • 932

Abstract


  • This paper considers parameters estimation for nonlinear and non-Gaussian state-space models with correlation. We propose an asymptotic quasi-likelihood (AQL) approach which utilises a nonparameteric kernel estimator of conditional variance covariance matrix \Sigma_t to replace the true \Sigma_t in the standard quasi-likelihood. The Kernel estimation avoids the risk of potential miss-specification of \Sigma_t and thus makes the parameter estimator more robust. This has been further verified by empirical studies carried out in this paper.

Authors


Publication Date


  • 2007

Citation


  • Alzghool, R. & Lin, Y. (2007). Asymptotic quasi-likelihood based on kernel smoothing for nonlinear and non-gaussian state-space models. The 2007 International Conference of Computational Statistics and Data Engineering. World Congress on Engineering (pp. 926-932). London: Newswood Limited, International Association of Engineers.

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=10350&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/3014

Start Page


  • 926

End Page


  • 932