  We consider the problem of multivariate density deconvolution when the
interest lies in estimating the distribution of a vector-valued random variable
but precise measurements of the variable of interest are not available,
observations being contaminated with additive measurement errors. 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 is not known but
replicated proxies are available for each unobserved value of the random
vector. Additionally, we allow the variability of the measurement errors to
depend on the associated unobserved value of the vector of interest through
unknown relationships which also automatically includes the case of
multivariate multiplicative measurement errors. Basic properties of finite
mixture models, multivariate normal kernels and exchangeable priors are
exploited in many novel ways to meet the modeling and computational challenges.
Theoretical results that show the flexibility of the proposed methods are
provided. We illustrate the efficiency of the proposed methods in recovering
the true density of interest through simulation experiments. The methodology is
applied to estimate the joint consumption pattern of different dietary
components from contaminated 24 hour recalls.
