Full Bayesian Analysis for a Class of Jump-Diffusion Models

The Full Bayesian Significance Test (FBST) is adjusted for jump detection in a diffusion process. Under a natural parameterization, pure diffusion can be seen as a precise hypothesis. The evidence measure defined by FBST deals with absolutely continuous posterior distributions, when posterior rates...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2009-01, Vol.38 (8), p.1262-1271
Main Authors: Rifo, Laura L. R., Torres, Soledad
Format: Article
Language:English
Subjects:
Distribution theory
Exact sciences and technology
Full Bayesian significance test
General topics
Jump-diffusion process
Markov processes
Mathematics
Primary 62F15
Probability and statistics
Probability theory and stochastic processes
Sciences and techniques of general use
Secondary 60J60
Statistics
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Summary:The Full Bayesian Significance Test (FBST) is adjusted for jump detection in a diffusion process. Under a natural parameterization, pure diffusion can be seen as a precise hypothesis. The evidence measure defined by FBST deals with absolutely continuous posterior distributions, when posterior rates for precise hypotheses are not appropriate. Applications to simulated and real data are shown.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610920802395694