IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v74y2014icp17-25.html
   My bibliography  Save this article

Improving mixture tree construction using better EM algorithms

Author

Listed:
  • Chen, Shu-Chuan (Grace)
  • Lindsay, Bruce

Abstract

This paper is concerned with hierarchical clustering of long binary sequence data. We propose two alternative improvements of the EM algorithm used in Chen and Lindsay (2006). One is the FixEM. It is just the regular EM but we no longer update the weights πs used in the ancestral mixture models. The other is the ModalEM. In this we cluster data according to the modes of an estimated density function for the data. In order to compare these methods with each other and other popular hierarchical clustering methods, we use a data example from the international HapMap project. We compare the speed and the ability of these methods to separate out true clusters. In addition, simulation studies are performed to compare the efficiency and accuracy of these methods. Our conclusion is that the new EM methods are far superior to the original, and that both provide useful alternatives to other standard clustering methods.

Suggested Citation

  • Chen, Shu-Chuan (Grace) & Lindsay, Bruce, 2014. "Improving mixture tree construction using better EM algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 17-25.
    • Handle: RePEc:eee:csdana:v:74:y:2014:i:c:p:17-25
    DOI: 10.1016/j.csda.2013.11.010
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947313004489
    Download Restriction: Full text for ScienceDirect subscribers only.
    File URL: https://libkey.io/10.1016/j.csda.2013.11.010?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. Hunter D.R. & Lange K., 2004. "A Tutorial on MM Algorithms," The American Statistician, American Statistical Association, vol. 58, pages 30-37, February.
    2. Shu-Chuan Chen & Bruce G. Lindsay, 2006. "Building mixture trees from binary sequence data," Biometrika, Biometrika Trust, vol. 93(4), pages 843-860, December.
    3. Berlinet, A.F. & Roland, Ch., 2012. "Acceleration of the EM algorithm: P-EM versus epsilon algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4122-4137.
    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. Baran, Sándor, 2014. "Probabilistic wind speed forecasting using Bayesian model averaging with truncated normal components," Computational Statistics & Data Analysis, Elsevier, vol. 75(C), pages 227-238.

    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. de Leeuw, Jan & Lange, Kenneth, 2009. "Sharp quadratic majorization in one dimension," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2471-2484, May.
    2. Rasmus Lentz & Jean Marc Robin & Suphanit Piyapromdee, 2018. "On Worker and Firm Heterogeneity in Wages and Employment Mobility: Evidence from Danish Register Data," 2018 Meeting Papers 469, Society for Economic Dynamics.
    3. Deng, Lifeng & Ding, Jieli & Liu, Yanyan & Wei, Chengdong, 2018. "Regression analysis for the proportional hazards model with parameter constraints under case-cohort design," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 194-206.
    4. Jurgen A. Doornik, 2018. "Accelerated Estimation of Switching Algorithms: The Cointegrated VAR Model and Other Applications," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 45(2), pages 283-300, June.
    5. Stéphane Chrétien & Alfred Hero & Hervé Perdry, 2012. "Space alternating penalized Kullback proximal point algorithms for maximizing likelihood with nondifferentiable penalty," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(4), pages 791-809, August.
    6. Green, Beth L. & Ayoub, Catherine & Bartlett, Jessica Dym & Von Ende, Adam & Furrer, Carrie & Chazan-Cohen, Rachel & Vallotton, Claire & Klevens, Joanne, 2014. "The effect of Early Head Start on child welfare system involvement: A first look at longitudinal child maltreatment outcomes," Children and Youth Services Review, Elsevier, vol. 42(C), pages 127-135.
    7. Xiaotian Zhu & David R. Hunter, 2019. "Clustering via finite nonparametric ICA mixture 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. 13(1), pages 65-87, March.
    8. Xifen Huang & Jinfeng Xu & Yunpeng Zhou, 2022. "Profile and Non-Profile MM Modeling of Cluster Failure Time and Analysis of ADNI Data," Mathematics, MDPI, vol. 10(4), pages 1-21, February.
    9. Matthew Pietrosanu & Jueyu Gao & Linglong Kong & Bei Jiang & Di Niu, 2021. "Advanced algorithms for penalized quantile and composite quantile regression," Computational Statistics, Springer, vol. 36(1), pages 333-346, March.
    10. Rasmus Lentz & Suphanit Piyapromdee & Jean-Marc Robin, 2022. "The Anatomy of Sorting - Evidence from Danish Data," SciencePo Working papers Main hal-03869383, HAL.
    11. Nguyen Thai An & Daniel Giles & Nguyen Mau Nam & R. Blake Rector, 2016. "The Log-Exponential Smoothing Technique and Nesterov’s Accelerated Gradient Method for Generalized Sylvester Problems," Journal of Optimization Theory and Applications, Springer, vol. 168(2), pages 559-583, February.
    12. Spyridon Samothrakis & Maria Fasli & Diego Perez & Simon Lucas, 2017. "Default policies for global optimisation of noisy functions with severe noise," Journal of Global Optimization, Springer, vol. 67(4), pages 893-907, April.
    13. Groenen, P.J.F. & Nalbantov, G.I. & Bioch, J.C., 2007. "SVM-Maj: a majorization approach to linear support vector machines with different hinge errors," Econometric Institute Research Papers EI 2007-49, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
    14. Songfeng Zheng, 2021. "KLERC: kernel Lagrangian expectile regression calculator," Computational Statistics, Springer, vol. 36(1), pages 283-311, March.
    15. Ziping Zhao & Daniel P. Palomar, 2017. "Robust Maximum Likelihood Estimation of Sparse Vector Error Correction Model," Papers 1710.05513, arXiv.org.
    16. Sakyajit Bhattacharya & Paul McNicholas, 2014. "A LASSO-penalized BIC for mixture model selection," 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. 8(1), pages 45-61, March.
    17. Hien Nguyen & Geoffrey McLachlan, 2015. "Maximum likelihood estimation of Gaussian mixture models without matrix operations," 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. 9(4), pages 371-394, December.
    18. Nguyen Thai An & Nguyen Mau Nam & Xiaolong Qin, 2020. "Solving k-center problems involving sets based on optimization techniques," Journal of Global Optimization, Springer, vol. 76(1), pages 189-209, January.
    19. Florian Schwendinger & Bettina Grün & Kurt Hornik, 2021. "A comparison of optimization solvers for log binomial regression including conic programming," Computational Statistics, Springer, vol. 36(3), pages 1721-1754, September.
    20. Emilie Chouzenoux & Jean-Christophe Pesquet & Audrey Repetti, 2014. "Variable Metric Forward–Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function," Journal of Optimization Theory and Applications, Springer, vol. 162(1), pages 107-132, July.

    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:74:y:2014:i:c:p:17-25. 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.