A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling
作者:
BradleyP. Carlin,
NicholasG. Polson,
DavidS. Stoffer,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1992)
卷期:
Volume 87,
issue 418
页码: 493-500
ISSN:0162-1459
年代: 1992
DOI:10.1080/01621459.1992.10475231
出版商: Taylor & Francis Group
关键词: Forecasting;Gibbs sampler;Kalman filter;Smoothing
数据来源: Taylor
摘要:
A solution to multivariate state-space modeling, forecasting, and smoothing is discussed. We allow for the possibilities of nonnormal errors and nonlinear functionals in the state equation, the observational equation, or both. An adaptive Monte Carlo integration technique known as the Gibbs sampler is proposed as a mechanism for implementing a conceptually and computationally simple solution in such a framework. The methodology is a general strategy for obtaining marginal posterior densities of coefficients in the model or of any of the unknown elements of the state space. Missing data problems (including thek-step ahead prediction problem) also are easily incorporated into this framework. We illustrate the broad applicability of our approach with two examples: a problem involving nonnormal error distributions in a linear model setting and a one-step ahead prediction problem in a situation where both the state and observational equations are nonlinear and involve unknown parameters.
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