Bayesian Semiparametric Multivariate Density Deconvolution.

ABSTRACT: We consider the problem of multivariate density deconvolution when interest lies in estimating the distribution of a vector valued random variable X but precise measurements on X are not available, observations being contaminated by measurement errors U. The existing sparse literature on the problem assumes the density of the measurement errors to be completely known. We propose robust Bayesian semiparametric multivariate deconvolution approaches when the measurement error density of U is not known but replicated proxies are available for at least some individuals. Additionally, we allow the variability of U to depend on the associated unobserved values of X through unknown relationships, which also automatically includes the case of multivariate m

SUBMITTER: Sarkar A 

PROVIDER: S-EPMC6075844 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature