IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v64y2008i2p508-518.html
   My bibliography  Save this article

Minimum Hellinger Distance Estimation for k-Component Poisson Mixture with Random Effects

Author

Listed:
  • Liming Xiang
  • Kelvin K. W. Yau
  • Yer Van Hui
  • Andy H. Lee

Abstract

No abstract is available for this item.

Suggested Citation

  • Liming Xiang & Kelvin K. W. Yau & Yer Van Hui & Andy H. Lee, 2008. "Minimum Hellinger Distance Estimation for k-Component Poisson Mixture with Random Effects," Biometrics, The International Biometric Society, vol. 64(2), pages 508-518, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:508-518
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00920.x
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Foxman, B., 1990. "Recurring urinary tract infection: Incidence and risk factors," American Journal of Public Health, American Public Health Association, vol. 80(3), pages 331-333.
    2. Murray Aitkin, 1999. "A General Maximum Likelihood Analysis of Variance Components in Generalized Linear Models," Biometrics, The International Biometric Society, vol. 55(1), pages 117-128, March.
    3. Marianthi Markatou, 2000. "Mixture Models, Robustness, and the Weighted Likelihood Methodology," Biometrics, The International Biometric Society, vol. 56(2), pages 483-486, June.
    4. Zudi Lu & Yer Van Hui & Andy H. Lee, 2003. "Minimum Hellinger Distance Estimation for Finite Mixtures of Poisson Regression Models and Its Applications," Biometrics, The International Biometric Society, vol. 59(4), pages 1016-1026, December.
    5. Karlis, Dimitris & Xekalaki, Evdokia, 1998. "Minimum Hellinger distance estimation for Poisson mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 29(1), pages 81-103, November.
    6. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    7. Emmanuel Lesaffre & Bart Spiessens, 2001. "On the effect of the number of quadrature points in a logistic random effects model: an example," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(3), pages 325-335.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Xiang, Liming & Yau, Kelvin K.W. & Lee, Andy H., 2012. "The robust estimation method for a finite mixture of Poisson mixed-effect models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1994-2005.
    2. Li Liu & Liming Xiang, 2014. "Semiparametric estimation in generalized linear mixed models with auxiliary covariates: A pairwise likelihood approach," Biometrics, The International Biometric Society, vol. 70(4), pages 910-919, December.
    3. Dalei Yu, 2016. "Conditional Akaike Information Criteria for a Class of Poisson Mixture Models with Random Effects," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1214-1235, December.

    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. Qingguo Tang & R. J. Karunamuni, 2018. "Robust variable selection for finite mixture regression models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 70(3), pages 489-521, June.
    2. Tang, Qingguo & Karunamuni, Rohana J., 2013. "Minimum distance estimation in a finite mixture regression model," Journal of Multivariate Analysis, Elsevier, vol. 120(C), pages 185-204.
    3. Takada, Teruko, 2009. "Simulated minimum Hellinger distance estimation of stochastic volatility models," Computational Statistics & Data Analysis, Elsevier, vol. 53(6), pages 2390-2403, April.
    4. 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.
    5. Wu, Jingjing & Karunamuni, Rohana J., 2012. "Efficient Hellinger distance estimates for semiparametric models," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 1-23.
    6. Jingjing Wu & Tasnima Abedin & Qiang Zhao, 2023. "Semiparametric modelling of two-component mixtures with stochastic dominance," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 75(1), pages 39-70, February.
    7. Vock, David & Davidian, Marie & Tsiatis, Anastasios, 2014. "SNP_NLMM: A SAS Macro to Implement a Flexible Random Effects Density for Generalized Linear and Nonlinear Mixed Models," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 56(c02).
    8. Luca Greco, 2022. "Robust fitting of mixtures of GLMs by weighted likelihood," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(1), pages 25-48, March.
    9. Alejandra Tapia & Victor Leiva & Maria del Pilar Diaz & Viviana Giampaoli, 2019. "Influence diagnostics in mixed effects logistic regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 920-942, September.
    10. Julian P. T. Higgins & Simon G. Thompson & David J. Spiegelhalter, 2009. "A re‐evaluation of random‐effects meta‐analysis," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 172(1), pages 137-159, January.
    11. Jorge I. Figueroa-Zúñiga & Cristian L. Bayes & Víctor Leiva & Shuangzhe Liu, 2022. "Robust beta regression modeling with errors-in-variables: a Bayesian approach and numerical applications," Statistical Papers, Springer, vol. 63(3), pages 919-942, June.
    12. Yao, Weixin & Wei, Yan & Yu, Chun, 2014. "Robust mixture regression using the t-distribution," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 116-127.
    13. Altaf H Khan, 2019. "An Application of Sinc Function based Quadrature Method in Statistical Models," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(4), pages 91-96, May.
    14. Rufo, M.J. & Pérez, C.J. & Martín, J., 2009. "Local parametric sensitivity for mixture models of lifetime distributions," Reliability Engineering and System Safety, Elsevier, vol. 94(7), pages 1238-1244.
    15. Getachew A. Dagne, 2016. "A growth mixture Tobit model: application to AIDS studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(7), pages 1174-1185, July.
    16. Marino, Maria Francesca & Alfó, Marco, 2016. "Gaussian quadrature approximations in mixed hidden Markov models for longitudinal data: A simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 94(C), pages 193-209.
    17. Suzanne J Carroll & Michael J Dale & Theophile Niyonsenga & Anne W Taylor & Mark Daniel, 2020. "Associations between area socioeconomic status, individual mental health, physical activity, diet and change in cardiometabolic risk amongst a cohort of Australian adults: A longitudinal path analysis," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-16, May.
    18. Francesco BARTOLUCCI & Silvia BACCI & Claudia PIGINI, 2015. "A Misspecification Test for Finite-Mixture Logistic Models for Clustered Binary and Ordered Responses," Working Papers 410, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    19. M. Rufo & J. Martín & C. Pérez, 2006. "Bayesian analysis of finite mixture models of distributions from exponential families," Computational Statistics, Springer, vol. 21(3), pages 621-637, December.
    20. Hammon, Angelina & Zinn, Sabine, 2020. "Multiple imputation of binary multilevel missing not at random data," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 69(3), pages 547-564.

    More about this item

    Statistics

    Access and download statistics

    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:bla:biomet:v:64:y:2008:i:2:p:508-518. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.