In this paper we study posterior asymptotics for conditional density estimation in the supremum L1 norm. Compared to the expected L1 norm, the supremum L1 norm allows accurate prediction at any designated conditional density. We model the conditional density as a regression tree by defining a data dependent sequence of increasingly finer partitions of the predictor space and by specifying the conditional density to be the same across all predictor values in a partition set. Each conditional density is modeled independently so that the prior specifies a type of dependence between conditional densities which disappears after a certain number of observations have been observed. The rate at which the number of partition sets increases with the sample size determines when the dependence between pairs of conditional densities is set to zero and, ultimately, drives posterior convergence at the true data distribution.

Posterior asymptotics in the supremum L1 norm for conditional density estimation

DE BLASI, Pierpaolo;
2016

Abstract

In this paper we study posterior asymptotics for conditional density estimation in the supremum L1 norm. Compared to the expected L1 norm, the supremum L1 norm allows accurate prediction at any designated conditional density. We model the conditional density as a regression tree by defining a data dependent sequence of increasingly finer partitions of the predictor space and by specifying the conditional density to be the same across all predictor values in a partition set. Each conditional density is modeled independently so that the prior specifies a type of dependence between conditional densities which disappears after a certain number of observations have been observed. The rate at which the number of partition sets increases with the sample size determines when the dependence between pairs of conditional densities is set to zero and, ultimately, drives posterior convergence at the true data distribution.
ELECTRONIC JOURNAL OF STATISTICS
10
3219
3246
http://projecteuclid.org/euclid.ejs/1479287219
Nonparametric Bayesian inference, posterior asymptotics, conditional density estimation, regression tree model
Pierpaolo De, Blasi; Stephen, G. Walker
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1620321
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