Parameterizing mixture models with generalized moments

Zhiyue Huang () and Paul Marriott ()

Annals of the Institute of Statistical Mathematics, 2016, vol. 68, issue 2, 269-297

Abstract: This paper considers a new way of parameterizing mixture models where parameters are interpreted as the generalized moments of the mixing distribution. Following a dimensionality reduction approach, approximate models have a finite-dimensional parameter with a corresponding parameter space: a moment space. The geometry of the moment space is studied and we derive the properties of the reconstructed mixing distributions. Links between the reparameterization and estimation methods for mixture models are also briefly discussed. Copyright The Institute of Statistical Mathematics, Tokyo 2016

Keywords: Moments; Chebyshev system; Local mixture models; Functional principle component analysis (search for similar items in EconPapers)
Date: 2016
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DOI: 10.1007/s10463-014-0490-9

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