Bayesian diffusion process models with time-varying parameters

Title
Bayesian diffusion process models with time-varying parameters
Author(s)
김용구강석복마크 벌리너[마크 벌리너]
Keywords
MAXIMUM-LIKELIHOOD-ESTIMATION; MONTE-CARLO METHODS; INFERENCE
Issue Date
201203
Publisher
KOREAN STATISTICAL SOC
Citation
JOURNAL OF THE KOREAN STATISTICAL SOCIETY, v.41, no.1, pp.137 - 144
Abstract
The diffusion process is a widely used statistical model for many natural dynamic phenomena but its inference is very complicated because complete data describing the diffusion sample path is not necessarily available. In addition, data is often collected with substantial uncertainty and it is not uncommon to have missing observations. Thus, the observed process will be discrete over a finite time period and the marginal likelihood given by this discrete data is not always available. In this paper, we consider a class of nonstationary diffusion process models with not only the measurement error but also discretely time-varying parameters which are modeled via a state space model. Hierarchical Bayesian inference for such a diffusion process model with time-varying parameters is applied to financial data. (C) 2011 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
URI
http://hdl.handle.net/YU.REPOSITORY/29614http://dx.doi.org/10.1016/j.jkss.2011.08.001
ISSN
1226-3192
Appears in Collections:
이과대학 > 통계학과 > Articles
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