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Order-based dependent Dirichlet processes
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DOI：10.1198/016214505000000727.png)
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Abstract
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In this article we propose a new framework for Bayesian nonparametric modeling with continuous covariates. In particular. we allow the nonparametric distribution to depend on covariates through ordering the random variables building the weights in the stick-breaking representation. We focus mostly on the class of random distributions that induces a Dirichlet process at each covariate value. We derive the correlation between distributions at different covariate values and use a point process to implement a practically useful type of ordering, Two main constructions with analytically known correlation structures are proposed. Practical and efficient computational methods are introduced. We apply our framework, through mixtures of these processes, to regression modeling, the modeling of stochastic volatility in time series data, and spatial geostatistical modeling.
Keywords:
Bayesian nonparametrics
Markov chain Monte Carlo
nonparametric regression
spatial modeling
stick-breaking priors
volatility modeling
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