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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Nonlinear Filters: Estimation and Applications

Nonlinear Filters: Estimation and Applications

Tanizaki, Hisashi

Hardcover

Business GeneralEconomicsProbability & Statistics

ISBN10: 3540613269
ISBN13: 9783540613268
Publisher: Springer Nature
Published: Aug 16 1996
Pages: 256
Weight: 1.25
Height: 0.69 Width: 6.14 Depth: 9.21
Language: English
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.

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