IDEAS home Printed from https://ideas.repec.org/a/spr/aistmt/v68y2016i2p269-297.html
   My bibliography  Save this article

Parameterizing mixture models with generalized moments

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s10463-014-0490-9
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s10463-014-0490-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Paul Marriott, 2002. "On the local geometry of mixture models," Biometrika, Biometrika Trust, vol. 89(1), pages 77-93, March.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Madeleine Cule & Richard Samworth & Michael Stewart, 2010. "Maximum likelihood estimation of a multi‐dimensional log‐concave density," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(5), pages 545-607, November.
    2. Zhang, Zhehao, 2018. "Renewal sums under mixtures of exponentials," Applied Mathematics and Computation, Elsevier, vol. 337(C), pages 281-301.
    3. Okhli, Kheirolah & Jabbari Nooghabi, Mehdi, 2023. "On the three-component mixture of exponential distributions: A Bayesian framework to model data with multiple lower and upper outliers," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 480-500.
    4. Balabdaoui, Fadoua & Kulagina, Yulia, 2020. "Completely monotone distributions: Mixing, approximation and estimation of number of species," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).
    5. Chung, Yeojin & Lindsay, Bruce G., 2015. "Convergence of the EM algorithm for continuous mixing distributions," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 190-195.
    6. Wang, Yong, 2008. "Dimension-reduced nonparametric maximum likelihood computation for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2388-2402, January.
    7. Chee, Chew-Seng, 2017. "A mixture model-based nonparametric approach to estimating a count distribution," Computational Statistics & Data Analysis, Elsevier, vol. 109(C), pages 34-44.
    8. Seo, Byungtae, 2017. "The doubly smoothed maximum likelihood estimation for location-shifted semiparametric mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 108(C), pages 27-39.
    9. Brentnall, Adam R. & Crowder, Martin J. & Hand, David J., 2011. "Approximate repeated-measures shrinkage," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1150-1159, February.
    10. Seo, Byungtae & Ha, Il Do, 2024. "Semiparametric accelerated failure time models under unspecified random effect distributions," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).
    11. Xiang, Sijia & Yao, Weixin & Seo, Byungtae, 2016. "Semiparametric mixture: Continuous scale mixture approach," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 413-425.
    12. Wang, Yong, 2010. "Fisher scoring: An interpolation family and its Monte Carlo implementations," Computational Statistics & Data Analysis, Elsevier, vol. 54(7), pages 1744-1755, July.
    13. Xu, Danli & Wang, Yong, 2023. "Density estimation for spherical data using nonparametric mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 182(C).
    14. Chee, Chew-Seng & Wang, Yong, 2016. "Nonparametric estimation of species richness using discrete k-monotone distributions," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 107-118.
    15. Ryan Martin, 2021. "A Survey of Nonparametric Mixing Density Estimation via the Predictive Recursion Algorithm," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(1), pages 97-121, May.
    16. Chee, Chew-Seng & Wang, Yong, 2013. "Minimum quadratic distance density estimation using nonparametric mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 1-16.
    17. Tzougas, George & Karlis, Dimitris & Frangos, Nicholas, 2017. "Confidence intervals of the premiums of optimal Bonus Malus Systems," LSE Research Online Documents on Economics 70926, London School of Economics and Political Science, LSE Library.
    18. Okhli, Kheirolah & Jabbari Nooghabi, Mehdi, 2021. "On the contaminated exponential distribution: A theoretical Bayesian approach for modeling positive-valued insurance claim data with outliers," Applied Mathematics and Computation, Elsevier, vol. 392(C).
    19. Chee, Chew-Seng & Wang, Yong, 2014. "Least squares estimation of a k-monotone density function," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 209-216.
    20. Martin, Ryan & Han, Zhen, 2016. "A semiparametric scale-mixture regression model and predictive recursion maximum likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 75-85.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aistmt:v:68:y:2016:i:2:p:269-297. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.