Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations

We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sam...

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Main Authors:	Mariano, Roberto S., Tanizaki, Hisashi
格式:	text
語言:	English
出版:	Institutional Knowledge at Singapore Management University 1998
主題:	Economics
在線閱讀:	https://ink.library.smu.edu.sg/soe_research/272
標簽:	添加標簽沒有標簽, 成為第一個標記此記錄!
機構:	Singapore Management University
語言:	English

id	sg-smu-ink.soe_research-1271
record_format	dspace
spelling	sg-smu-ink.soe_research-12712010-09-23T05:48:03Z Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations Mariano, Roberto S. Tanizaki, Hisashi We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sampling. The proposed filters are extended to prediction and smoothing algorithms. Monte Carlo experiments are carried out to assess the statistical merits of the proposed filters. 1998-01-01T08:00:00Z text https://ink.library.smu.edu.sg/soe_research/272 info:doi/10.1016/s0304-4076(97)80226-6 Research Collection School Of Economics eng Institutional Knowledge at Singapore Management University Economics
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Economics
spellingShingle	Economics Mariano, Roberto S. Tanizaki, Hisashi Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
description	We propose two nonlinear and nonnormal filters based on Monte Carlo simulation techniques. In terms of programming and computational requirements both filters are more tractable than other nonlinear filters that use numerical integration, Monte Carlo integration with importance sampling or Gibbs sampling. The proposed filters are extended to prediction and smoothing algorithms. Monte Carlo experiments are carried out to assess the statistical merits of the proposed filters.
format	text
author	Mariano, Roberto S. Tanizaki, Hisashi
author_facet	Mariano, Roberto S. Tanizaki, Hisashi
author_sort	Mariano, Roberto S.
title	Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_short	Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_full	Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_fullStr	Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_full_unstemmed	Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations
title_sort	nonlinear and non-gaussian state-space modeling with monte-carlo simulations
publisher	Institutional Knowledge at Singapore Management University
publishDate	1998
url	https://ink.library.smu.edu.sg/soe_research/272
_version_	1770569094737690624

Nonlinear and Non-Gaussian State-Space Modeling with Monte-Carlo Simulations

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