IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v78y2008i15p2332-2338.html
   My bibliography  Save this article

Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data

Author

Listed:
  • Kuroda, Masahiro
  • Sakakihara, Michio
  • Geng, Zhi

Abstract

In this paper, we discuss the MLEs for log-linear models with partially classified data. We propose to apply the Aitken [delta]2 method of Aitken [Aitken, A.C., 1926. On Bernoulli's numerical solution of algebraic equations. Proc. R. Soc. Edinburgh 46, 289-305] to the EM and ECM algorithms to accelerate their convergence. The Aitken [delta]2 accelerated algorithm shares desirable properties of the EM algorithm, such as numerical stability, computational simplicity and flexibility in interpreting the incompleteness of data. We show the convergence of the Aitken [delta]2 accelerated algorithm and compare its speed of convergence with that of the EM algorithm, and we also illustrate their performance by means of a simulation.

Suggested Citation

  • Kuroda, Masahiro & Sakakihara, Michio & Geng, Zhi, 2008. "Acceleration of the EM and ECM algorithms using the Aitken [delta]2 method for log-linear models with partially classified data," Statistics & Probability Letters, Elsevier, vol. 78(15), pages 2332-2338, October.
    • Handle: RePEc:eee:stapro:v:78:y:2008:i:15:p:2332-2338
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(08)00130-2
    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. Lauritzen, Steffen L., 1995. "The EM algorithm for graphical association models with missing data," Computational Statistics & Data Analysis, Elsevier, vol. 19(2), pages 191-201, February.
    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. Kwun Chuen Gary Chan, 2017. "Acceleration of Expectation-Maximization algorithm for length-biased right-censored data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 102-112, January.

    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. Chen, Yen-Liang & Hu, Hui-Ling, 2006. "An overlapping cluster algorithm to provide non-exhaustive clustering," European Journal of Operational Research, Elsevier, vol. 173(3), pages 762-780, September.
    2. Croft, J. & Smith, J. Q., 2003. "Discrete mixtures in Bayesian networks with hidden variables: a latent time budget example," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 539-547, January.
    3. Fazia Abdat & Sylvie Leclercq & Xavier Cuny & Claire Tissot, 2014. "Extracting recurrent scenarios from narrative texts using a Bayesian network: Application to serious occupational accidents with movement disturbance," Post-Print hal-01578382, HAL.
    4. Arentze, Theo & Timmermans, Harry, 2009. "Regimes in social-cultural events-driven activity sequences: Modelling approach and empirical application," Transportation Research Part A: Policy and Practice, Elsevier, vol. 43(4), pages 311-322, May.
    5. Langseth, Helge & Portinale, Luigi, 2007. "Bayesian networks in reliability," Reliability Engineering and System Safety, Elsevier, vol. 92(1), pages 92-108.
    6. Christopher Raphael, 2003. "Bayesian Networks with Degenerate Gaussian Distributions," Methodology and Computing in Applied Probability, Springer, vol. 5(2), pages 235-263, June.
    7. Esma Nur Cinicioglu & Gül Huyugüzel Kışla & A. Özlem Önder & Y. Gülnur Muradoğlu, 2024. "The Changing Behavior of the European Credit Default Swap Spreads During the Covid-19 Pandemic: A Bayesian Network Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 63(3), pages 1213-1254, March.
    8. Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    9. Hongyu Wang & Jian Tang & Pengpeng Xu & Rundong Chen & Haona Yao, 2022. "Research on the Influence Mechanism of Street Vitality in Mountainous Cities Based on a Bayesian Network: A Case Study of the Main Urban Area of Chongqing," Land, MDPI, vol. 11(5), pages 1-22, May.
    10. Minjeong Jeon & Frank Rijmen & Sophia Rabe-Hesketh, 2013. "Modeling Differential Item Functioning Using a Generalization of the Multiple-Group Bifactor Model," Journal of Educational and Behavioral Statistics, , vol. 38(1), pages 32-60, February.
    11. Claudia Tarantola & Paola Vicard & Ioannis Ntzoufras, 2012. "Monitoring and Improving Greek Banking Services Using Bayesian Networks: an Analysis of Mystery Shopping Data," Quaderni di Dipartimento 160, University of Pavia, Department of Economics and Quantitative Methods.
    12. Sheehan, Barry & Murphy, Finbarr & Mullins, Martin & Ryan, Cian, 2019. "Connected and autonomous vehicles: A cyber-risk classification framework," Transportation Research Part A: Policy and Practice, Elsevier, vol. 124(C), pages 523-536.
    13. Astrid Kemperman & Pauline van den Berg & Minou Weijs-Perrée & Kevin Uijtdewillegen, 2019. "Loneliness of Older Adults: Social Network and the Living Environment," IJERPH, MDPI, vol. 16(3), pages 1-16, January.
    14. Yawen Sun & Shaohua Tan & Qixiao He & Jize Shen, 2022. "Influence Mechanisms of Community Sports Parks to Enhance Social Interaction: A Bayesian Belief Network Analysis," IJERPH, MDPI, vol. 19(3), pages 1-22, January.
    15. Jie Fan & Baoyin Liu & Xiaodong Ming & Yong Sun & Lianjie Qin, 2022. "The amplification effect of unreasonable human behaviours on natural disasters," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-10, December.
    16. Xingguang Wu & Huirong Huang & Weichao Yu & Yuming Lin & Yanhui Xue & Qingwen Cai & Jili Xu, 2024. "Accident analysis and risk prediction of tank farm based on Bayesian network method," Journal of Risk and Reliability, , vol. 238(2), pages 366-386, April.
    17. Garvey, Myles D. & Carnovale, Steven & Yeniyurt, Sengun, 2015. "An analytical framework for supply network risk propagation: A Bayesian network approach," European Journal of Operational Research, Elsevier, vol. 243(2), pages 618-627.
    18. Marco Di Zio & Mauro Scanu & Lucia Coppola & Orietta Luzi & Alessandra Ponti, 2004. "Bayesian networks for imputation," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 309-322, May.
    19. Kim, Seong-Ho & Kim, Sung-Ho, 2006. "A divide-and-conquer approach in applying EM for large recursive models with incomplete categorical data," Computational Statistics & Data Analysis, Elsevier, vol. 50(3), pages 611-641, February.
    20. Danilo Gambelli & Francesco Solfanelli & Raffaele Zanoli, 2014. "Feasibility of risk-based inspections in organic farming: results from a probabilistic model," Agricultural Economics, International Association of Agricultural Economists, vol. 45(3), pages 267-277, May.

    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:stapro:v:78:y:2008:i:15:p:2332-2338. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.