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2018 A deconvolution path for mixtures
Oscar-Hernan Madrid-Padilla, Nicholas G. Polson, James Scott
Electron. J. Statist. 12(1): 1717-1751 (2018). DOI: 10.1214/18-EJS1430


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 mi-xing 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 Scott. "A deconvolution path for mixtures." Electron. J. Statist. 12 (1) 1717 - 1751, 2018.


Received: 1 May 2017; Published: 2018
First available in Project Euclid: 29 May 2018

zbMATH: 06886382
MathSciNet: MR3806437
Digital Object Identifier: 10.1214/18-EJS1430

Primary: 62G05
Secondary: 62G07

Keywords: Deconvolution , Empirical Bayes , Mixture models , penalized likelihood , sensitivity analysis


Vol.12 • No. 1 • 2018
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