On a nonparametric recursive estimator of the mixing distribution

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On a nonparametric recursive estimator of the mixing distribution

Michael A. Newton .

To appear * Sankhya *

First issued August 2000 as Technical Report 1021, Department of Statistics,
University of Wisconsin, Madison.

### Abstract:

Routinely in statistical applications hierarchical
models arise in which unobserved random effects contribute to
heterogeneity amongst sampling units. An easily computable, smooth
nonparametric estimate of the underlying mixing distribution
can be derived as an approximate nonparametric Bayes estimate
under a Dirichlet process prior. I discuss the recursive
estimation algorithm, its consistency properties, and its application
in several examples, including its use as a model diagnostic
in the analysis of DNA microarray gene expression data.

Accepted
Version, 12/01

Original
Technical Report, 8/00