Nonlinear Filters: Estimation and Applications

by Tanizaki, Hisashi

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Title Statements

Full title :Nonlinear Filters : Estimation and Applications / by Hisashi Tanizaki.

Summary

Nonlinear and nonnormal filters are introduced and developed. Traditional nonlinear filters such as the extended Kalman filter and the Gaussian sum filter give biased filtering estimates, and therefore several nonlinear and nonnormal filters have been derived from the underlying probability density functions. The density-based nonlinear filters introduced in this book utilize numerical integration, Monte-Carlo integration with importance sampling or rejection sampling and the obtained filtering estimates are asymptotically unbiased and efficient. By Monte-Carlo simulation studies, all the nonlinear filters are compared. Finally, as an empirical application, consumption functions based on the rational expectation model are estimated for the nonlinear filters, where US, UK and Japan economies are compared.

Edition Statements

Publication Date:1996.
Edition:Second revised and enlarged edition

Publication Statements

Publisher(s):Springer Berlin Heidelberg
Place of Publication:Berlin, Heidelberg
Language:eng
Publication Date:1996.

The Physical Object

Description:1 online resource (xix, 255 pages)

Edition Identifiers

ISBN:9783662032237, 3662032236
DOI:10.1007/978-3-662-03223-7
oclc:ocn851372266
LC Classification:HB1-846.8
Dewey Decimal:330.1

Introduction -- State-Space Model in Linear Case -- Traditional Nonlinear Filters -- Density Based Nonlinear Filters -- Monte-Carlo Experiments -- Application of Ninlinear Filters -- Prediction and Smoothing -- Summary and Concluding Remarks.