IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v50y2006i2p551-567.html
   My bibliography  Save this article

Segmenting magnetic resonance images via hierarchical mixture modelling

Author

Listed:
  • Priebe, Carey E.
  • Miller, Michael I.
  • Tilak Ratnanather, J.

Abstract

No abstract is available for this item.

Suggested Citation

  • Priebe, Carey E. & Miller, Michael I. & Tilak Ratnanather, J., 2006. "Segmenting magnetic resonance images via hierarchical mixture modelling," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 551-567, January.
    • Handle: RePEc:eee:csdana:v:50:y:2006:i:2:p:551-567
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(04)00273-7
    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. Priebe, Carey E. & Marchette, David J., 2000. "Alternating kernel and mixture density estimates," Computational Statistics & Data Analysis, Elsevier, vol. 35(1), pages 43-65, November.
    2. G. J. McLachlan, 1987. "On Bootstrapping the Likelihood Ratio Test Statistic for the Number of Components in a Normal Mixture," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 318-324, November.
    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. Woo, Mi-Ja & Sriram, T.N., 2007. "Robust estimation of mixture complexity for count data," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4379-4392, May.
    2. Fetene B. Tekle & Dereje W. Gudicha & Jeroen K. Vermunt, 2016. "Power analysis for the bootstrap likelihood ratio test for the number of classes in latent class models," 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. 10(2), pages 209-224, June.
    3. Fernández, D. & Arnold, R. & Pledger, S., 2016. "Mixture-based clustering for the ordered stereotype model," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 46-75.
    4. Yuan Liu & Hongyun Liu, 2019. "Effects of Distance and Shape on the Estimation of the Piecewise Growth Mixture Model," Journal of Classification, Springer;The Classification Society, vol. 36(3), pages 659-677, October.
    5. Park, Seong C. & Brorsen, B. Wade & Stoecker, Arthur L. & Hattey, Jeffory A., 2012. "Forage Response to Swine Effluent: A Cox Nonnested Test of Alternative Functional Forms Using a Fast Double Bootstrap," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(4), pages 593-606, November.
    6. Sangyeol Lee & Taewook Lee, 2008. "Robust estimation for the order of finite mixture models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 68(3), pages 365-390, November.
    7. Geert Soete & Willem Heiser, 1993. "A latent class unfolding model for analyzing single stimulus preference ratings," Psychometrika, Springer;The Psychometric Society, vol. 58(4), pages 545-565, December.
    8. Un Jung Lee & ShengLi Tzeng & Yu-Chuan Chen & James J Chen, 2017. "Development of Predictive Signatures for Treatment Selection in Precision Medicine," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 2(4), pages 83-88, August.
    9. Hennig, Christian, 2008. "Dissolution point and isolation robustness: Robustness criteria for general cluster analysis methods," Journal of Multivariate Analysis, Elsevier, vol. 99(6), pages 1154-1176, July.
    10. Kanchewa, Stella & Christensen, Kirsten M. & Poon, Cyanea Y.S. & Parnes, McKenna & Schwartz, Sarah, 2021. "More than fun and games? Understanding the role of school-based mentor-mentee match activity profiles in relationship processes and outcomes," Children and Youth Services Review, Elsevier, vol. 120(C).
    11. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    12. 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.
    13. Marco Alfò & Giovanni Trovato, 2004. "Semiparametric Mixture Models for Multivariate Count Data, with Application," CEIS Research Paper 51, Tor Vergata University, CEIS.
    14. Dinghai Xu & John Knight, 2011. "Continuous Empirical Characteristic Function Estimation of Mixtures of Normal Parameters," Econometric Reviews, Taylor & Francis Journals, vol. 30(1), pages 25-50.
    15. Sebastian Vollmer & Hajo Holzmann & Florian Ketterer & Stephan Klasen & David Canning, 2013. "The Emergence of Three Human Development Clubs," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-7, March.
    16. Spiegel, Alisa & Slijper, Thomas & de Mey, Yann & Meuwissen, Miranda P.M. & Poortvliet, P. Marijn & Rommel, Jens & Hansson, Helena & Vigani, Mauro & Soriano, Bárbara & Wauters, Erwin & Appel, Franzisk, 2021. "Resilience capacities as perceived by European farmers," Agricultural Systems, Elsevier, vol. 193(C).
    17. Geert Soete & Wayne DeSarbo, 1991. "A latent class probit model for analyzing pick any/N data," Journal of Classification, Springer;The Classification Society, vol. 8(1), pages 45-63, January.
    18. Rainer Schlittgen, 2011. "A weighted least-squares approach to clusterwise regression," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 95(2), pages 205-217, June.
    19. Daniel McNeish & Jeffrey R. Harring, 2017. "The Effect of Model Misspecification on Growth Mixture Model Class Enumeration," Journal of Classification, Springer;The Classification Society, vol. 34(2), pages 223-248, July.
    20. Lo, Yungtai, 2011. "Bias from misspecification of the component variances in a normal mixture," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2739-2747, September.

    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:50:y:2006:i:2:p:551-567. 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.