Posterior consistency in conditional distribution estimation.
Posterior consistency in conditional distribution estimation.
复制标题
DOI：
10.1016/j.jmva.2013.01.011
复制
发表时间：
2013-04-01
影响因子：
1.6
通讯作者：
Tokdar, Surya T.
中科院分区：
文献类型：
--
作者：
Pati, Debdeep;Dunson, David B.;Tokdar, Surya T.
关键词：
A wide variety of priors have been proposed for nonparametric Bayesian estimation of conditional distributions, and there is a clear need for theorems providing conditions on the prior for large support, as well as posterior consistency. Estimation of an uncountable collection of conditional distributions across different regions of the predictor space is a challenging problem, which differs in some important ways from density and mean regression estimation problems. Defining various topologies on the space of conditional distributions, we provide sufficient conditions for posterior consistency focusing on a broad class of priors formulated as predictor-dependent mixtures of Gaussian kernels. This theory is illustrated by showing that the conditions are satisfied for a class of generalized stick-breaking process mixtures in which the stick-breaking lengths are monotone, differentiable functions of a continuous stochastic process. We also provide a set of sufficient conditions for the case where stick-breaking lengths are predictor independent, such as those arising from a fixed Dirichlet process prior.
登录
查看更多内容
影响因子：
1.8
作者：
Jara, Alejandro;Lesaffre, Emmanuel;Quintana, Fernando
通讯作者：
Quintana, Fernando
影响因子：
3.7
作者：
Chung Y;Dunson DB
通讯作者：
Dunson DB
DOI：
10.1198/016214501750332758
发表时间：
2001-03-01
影响因子：
3.7
作者：
Ishwaran, H;James, LF
通讯作者：
James, LF
影响因子：
4.5
作者：
FERGUSON, TS
通讯作者：
FERGUSON, TS
DOI：
10.1198/016214504000000205
发表时间：
2004-03-01
影响因子：
3.7
作者：
De Iorio, M;Müller, P;MacEachern, SN
通讯作者：
MacEachern, SN