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Creators/Authors contains: "Ritchie, Alexander"

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  1. ; ; (, Advances in neural information processing systems)
    null (Ed.)
    Recent research has established sufficient conditions for finite mixture models to be identifiablefrom grouped observations. These conditions allow the mixture components to be nonparametricand have substantial (or even total) overlap. This work proposes an algorithm that consistentlyestimates any identifiable mixture model from grouped observations. Our analysis leverages anoracle inequality for weighted kernel density estimators of the distribution on groups, togetherwith a general result showing that consistent estimation of the distribution on groups impliesconsistent estimation of mixture components. A practical implementation is provided for pairedobservations, and the approach is shown to outperform existing methods, especially when mixturecomponents overlap significantly. 
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    Accepted Manuscript Full Text Available
  2. ; ; (, 34th Conferene on Neural Information Processing Systems (NeurIPS 2020))
    null (Ed.)
    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. 
    more » « less
    Accepted Manuscript Full Text Available
  3. ; ; ; ; (, Proceedings of 2019 IEEE Data Science Workshop (DSW))
    Full Text Available