IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v46y2004i4p661-676.html
   My bibliography  Save this article

Drug risk assessment with determining the number of sub-populations under finite mixture normal models

Author

Listed:
  • Tao, Jian
  • Shi, Ning-Zhong
  • Lee, S.-Y.Sik-Yum

Abstract

No abstract is available for this item.

Suggested Citation

  • Tao, Jian & Shi, Ning-Zhong & Lee, S.-Y.Sik-Yum, 2004. "Drug risk assessment with determining the number of sub-populations under finite mixture normal models," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 661-676, July.
    • Handle: RePEc:eee:csdana:v:46:y:2004:i:4:p:661-676
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(03)00226-3
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Kenny S. Crump, 1995. "Calculation of Benchmark Doses from Continuous Data," Risk Analysis, John Wiley & Sons, vol. 15(1), pages 79-89, February.
    2. Biernacki, Christophe & Celeux, Gilles & Govaert, Gerard, 2003. "Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 561-575, January.
    3. Mehdi Razzaghi & Ralph L. Kodell, 2000. "Risk Assessment for Quantitative Responses Using a Mixture Model," Biometrics, The International Biometric Society, vol. 56(2), pages 519-527, June.
    4. Esben Budtz-Jørgensen & Niels Keiding & Philippe Grandjean, 2001. "Benchmark Dose Calculation from Epidemiological Data," Biometrics, The International Biometric Society, vol. 57(3), pages 698-706, September.
    5. Karlis, Dimitris & Xekalaki, Evdokia, 2003. "Choosing initial values for the EM algorithm for finite mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 577-590, January.
    6. Miloslavsky, Maja & van der Laan, Mark J., 2003. "Fitting of mixtures with unspecified number of components using cross validation distance estimate," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 413-428, January.
    7. Woodward, Wayne A. & Sain, Stephan R., 2003. "Testing for outliers from a mixture distribution when some data are missing," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 193-210, October.
    8. Ralph L. Kodell & Ronnie W. West, 1993. "Upper Confidence Limits on Excess Risk for Quantitative Responses," Risk Analysis, John Wiley & Sons, vol. 13(2), pages 177-182, April.
    9. anonymous, 2003. "Editorial Objectives," Management Science, INFORMS, vol. 49(1), pages 1-1, January.
    10. Chib S. & Jeliazkov I., 2001. "Marginal Likelihood From the Metropolis-Hastings Output," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 270-281, March.
    11. Song, Xin-Yuan & Lee, Sik-Yum, 2002. "A Bayesian model selection method with applications," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 539-557, September.
    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. Liu, Rui-Yin & Tao, Jian & Shi, Ning-Zhong & He, Xuming, 2011. "Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1498-1508, March.
    2. Zhangpeng Gao & Shahidur Rahman, 2006. "A New Direction of Fund Rating Based on the Finite Normal Mixture Model," Economic Growth Centre Working Paper Series 0603, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.

    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. Walter W. Piegorsch, 2010. "Translational benchmark risk analysis," Journal of Risk Research, Taylor & Francis Journals, vol. 13(5), pages 653-667, July.
    2. Signe M. Jensen & Felix M. Kluxen & Christian Ritz, 2019. "A Review of Recent Advances in Benchmark Dose Methodology," Risk Analysis, John Wiley & Sons, vol. 39(10), pages 2295-2315, October.
    3. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    4. Mehdi Razzaghi & Ralph Kodell, 2004. "Quantitative Risk Assessment for Developmental Neurotoxic Effects," Risk Analysis, John Wiley & Sons, vol. 24(6), pages 1673-1681, December.
    5. Liu, Rui-Yin & Tao, Jian & Shi, Ning-Zhong & He, Xuming, 2011. "Bayesian analysis of the patterns of biological susceptibility via reversible jump MCMC sampling," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1498-1508, March.
    6. Kan Shao & Jeffrey S. Gift, 2014. "Model Uncertainty and Bayesian Model Averaged Benchmark Dose Estimation for Continuous Data," Risk Analysis, John Wiley & Sons, vol. 34(1), pages 101-120, January.
    7. Adrian O’Hagan & Arthur White, 2019. "Improved model-based clustering performance using Bayesian initialization averaging," Computational Statistics, Springer, vol. 34(1), pages 201-231, March.
    8. Hung Tong & Cristina Tortora, 2022. "Model-based clustering and outlier detection with missing data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(1), pages 5-30, March.
    9. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "Erratum to: The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 327-355, July.
    10. Paolo Berta & Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini, 2016. "Multilevel cluster-weighted models for the evaluation of hospitals," METRON, Springer;Sapienza Università di Roma, vol. 74(3), pages 275-292, December.
    11. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    12. repec:jss:jstsof:28:i04 is not listed on IDEAS
    13. Saif Eddin Jabari & Nikolaos M. Freris & Deepthi Mary Dilip, 2020. "Sparse Travel Time Estimation from Streaming Data," Transportation Science, INFORMS, vol. 54(1), pages 1-20, January.
    14. Papastamoulis, Panagiotis & Martin-Magniette, Marie-Laure & Maugis-Rabusseau, Cathy, 2016. "On the estimation of mixtures of Poisson regression models with large number of components," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 97-106.
    15. Derek S. Young & Xi Chen & Dilrukshi C. Hewage & Ricardo Nilo-Poyanco, 2019. "Finite mixture-of-gamma distributions: estimation, inference, and model-based clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 13(4), pages 1053-1082, December.
    16. Kerekes, Monika, 2012. "Growth miracles and failures in a Markov switching classification model of growth," Journal of Development Economics, Elsevier, vol. 98(2), pages 167-177.
    17. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    18. Masahiro Kuroda & Zhi Geng & Michio Sakakihara, 2015. "Improving the vector $$\varepsilon $$ ε acceleration for the EM algorithm using a re-starting procedure," Computational Statistics, Springer, vol. 30(4), pages 1051-1077, December.
    19. Kerekes, Monika, 2009. "Growth miracles and failures in a Markov switching classification model of growth," Discussion Papers 2009/11, Free University Berlin, School of Business & Economics.
    20. Andrews, Jeffrey L., 2018. "Addressing overfitting and underfitting in Gaussian model-based clustering," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 160-171.
    21. Mai, Feng & Fry, Michael J. & Ohlmann, Jeffrey W., 2018. "Model-based capacitated clustering with posterior regularization," European Journal of Operational Research, Elsevier, vol. 271(2), pages 594-605.

    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:eee:csdana:v:46:y:2004:i:4:p:661-676. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.