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arXiv · 1511.06750

A deconvolution path for mixtures

Oscar Hernan Madrid Padilla·Nicholas G. Polson·James G. Scott
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Abstract

We propose a class of estimators for deconvolution in mixture models based on a simple two-step "bin-and-smooth" procedure applied to histogram counts. The method is both statistically and computationally efficient: by exploiting recent advances in convex optimization, we are able to provide a full deconvolution path that shows the estimate for the mixing distribution across a range of plausible degrees of smoothness, at far less cost than a full Bayesian analysis. This enables practitioners to conduct a sensitivity analysis with minimal effort. This is especially important for applied data analysis, given the ill-posed nature of the deconvolution problem. Our results establish the favorable theoretical properties of our estimator and show that it offers state-of-the-art performance when compared to benchmark methods across a range of scenarios.

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Oscar Hernan Madrid Padilla, Nicholas G. Polson, James G. Scott. 2017-05-26. A deconvolution path for mixtures. https://arxiv.org/abs/1511.06750

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