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A new sufficient condition for identifiability of countably infinite mixtures

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Listed:
  • Lei Yang
  • Xianyi Wu

Abstract

While identifiability of finite mixtures for a wide range of distributions has been studied by statisticians for decades, discussion on countably infinite mixtures is still limited. This article provides an sufficient condition by means of well-ordered sets and uniform convergence of series. It is then applied to revisit some examples for which the identifiability is well established and then explore the identifiability for several distribution families, including normal, gamma, Cauchy, noncentral $$\chi ^2$$ χ 2 , multivariate normal distributions. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Lei Yang & Xianyi Wu, 2014. "A new sufficient condition for identifiability of countably infinite mixtures," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(3), pages 377-387, April.
  • Handle: RePEc:spr:metrik:v:77:y:2014:i:3:p:377-387
    DOI: 10.1007/s00184-013-0444-x
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    References listed on IDEAS

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    1. Wu, Xianyi & Zhou, Xian, 2006. "A new characterization of distortion premiums via countable additivity for comonotonic risks," Insurance: Mathematics and Economics, Elsevier, vol. 38(2), pages 324-334, April.
    2. N. Atienza & J. Garcia-Heras & J. Muñoz-Pichardo, 2006. "A new condition for identifiability of finite mixture distributions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 63(2), pages 215-221, April.
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    Cited by:

    1. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).

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