Nonlinear and nonnormal filter using importance sampling: Antithetic Monte Carlo integration

被引：3
|
作者：
Tanizaki, H [1 ]
机构：
[1] Kobe Univ, Fac Econ, Nada Ku, Kobe, Hyogo 6578501, Japan
来源：
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 1999年 / 28卷 / 02期
关键词：
state-space model; filtering; Monte Carlo integration; importance sampling; antithetic Monte Carlo; resampling;
D O I：
10.1080/03610919908813560
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
In this paper, the importance sampling filter proposed by Mariano and Tanizaki (1995). Tanizaki (1996), Tanizaki and Mariano (1994) is extended using the antithetic Monte Carlo method to reduce the simulation errors. By;Monte Carlo studies, the importance sampling filter with the antithetic Monte Carlo method is compared with the importance sampling filter without the antithetic Monte Carlo method. It is shown that for all the simulation studies the former is clearly superior to the latter especially when number of random draws is small.
引用
收藏
页码：463 / 486
页数：24
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