Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations
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Publication:6342805
arXiv2006.07459MaRDI QIDQ6342805
Author name not available (Why is that?)
Publication date: 12 June 2020
Abstract: Recent research has established sufficient conditions for finite mixture models to be identifiable from grouped observations. These conditions allow the mixture components to be nonparametric and have substantial (or even total) overlap. This work proposes an algorithm that consistently estimates any identifiable mixture model from grouped observations. Our analysis leverages an oracle inequality for weighted kernel density estimators of the distribution on groups, together with a general result showing that consistent estimation of the distribution on groups implies consistent estimation of mixture components. A practical implementation is provided for paired observations, and the approach is shown to outperform existing methods, especially when mixture components overlap significantly.
Has companion code repository: https://github.com/aritchie9590/NDIGO
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