Latent Stick-Breaking Processes.
Abstract
We develop a model for stochastic processes with random marginal distributions. Our
model relies on a stick-breaking construction for the marginal distribution of the
process, and introduces dependence across locations by using a latent Gaussian copula
model as the mechanism for selecting the atoms. The resulting latent stick-breaking
process (LaSBP) induces a random partition of the index space, with points closer
in space having a higher probability of being in the same cluster. We develop an efficient
and straightforward Markov chain Monte Carlo (MCMC) algorithm for computation and
discuss applications in financial econometrics and ecology. This article has supplementary
material online.
Type
Journal articleSubject
Nonparametric BayesOption pricing
Point-referenced counts
Random probability measure
Random stochastic processes
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https://hdl.handle.net/10161/4401Published Version (Please cite this version)
10.1198/jasa.2010.tm08241Publication Info
(2010). Latent Stick-Breaking Processes. J Am Stat Assoc, 105(490). pp. 647-659. 10.1198/jasa.2010.tm08241. Retrieved from https://hdl.handle.net/10161/4401.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
David B. Dunson
Arts and Sciences Distinguished Professor of Statistical Science
My research focuses on developing new tools for probabilistic learning from complex
data - methods development is directly motivated by challenging applications in ecology/biodiversity,
neuroscience, environmental health, criminal justice/fairness, and more. We seek
to develop new modeling frameworks, algorithms and corresponding code that can be
used routinely by scientists and decision makers. We are also interested in new inference
framework and in studying theoretical properties
Alan E. Gelfand
James B. Duke Distinguished Professor Emeritus of Statistical Science
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