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Parameterizing mixture models with generalized moments

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  • Zhiyue Huang
  • Paul Marriott

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

Suggested Citation

  • Zhiyue Huang & Paul Marriott, 2016. "Parameterizing mixture models with generalized moments," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 68(2), pages 269-297, April.
    • Handle: RePEc:spr:aistmt:v:68:y:2016:i:2:p:269-297
    DOI: 10.1007/s10463-014-0490-9
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    References listed on IDEAS

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    1. Paul Marriott, 2002. "On the local geometry of mixture models," Biometrika, Biometrika Trust, vol. 89(1), pages 77-93, March.
    2. K. Anaya-Izquierdo & P. Marriott, 2007. "Local mixtures of the exponential distribution," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(1), pages 111-134, March.
    3. Yong Wang, 2007. "On fast computation of the non‐parametric maximum likelihood estimate of a mixing distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 185-198, April.
    4. Paul Marriott, 2007. "Extending local mixture models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 59(1), pages 95-110, March.
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