Monte Carlo approximations for general state-space models

被引：97

作者：

Hurzeler, M ^{[1
]}

Kunsch, HR ^{[1
]}

机构：

[1] ETH Zentrum, Seminar Stat, CH-8092 Zurich, Switzerland

来源：

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS | 1998年 / 7卷 / 02期

关键词：

Kalman filter; Kalman smoother; Monte Carlo methods; nonlinear time series analysis; robustness for time series; state-space models;

D O I：

10.2307/1390812

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：

Nonlinear and non-Gaussian state-space models form a large and flexible model class in time series analysis. Two methods for sequentially generating samples from filter densities and smoother densities by simple rejection algorithms are introduced. We illustrate the behavior of our methods in several nonlinear and non-Gaussian examples and compare them with other well-known methods.

引用

页码：175 / 193

页数：19

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