IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v112y2017i520p1598-1611.html
   My bibliography  Save this article

The Hamming Ball Sampler

Author

Listed:
  • Michalis K. Titsias
  • Christopher Yau

Abstract

We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inference in statistical models involving high-dimensional discrete state spaces. The sampling scheme uses an auxiliary variable construction that adaptively truncates the model space allowing iterative exploration of the full model space. The approach generalizes conventional Gibbs sampling schemes for discrete spaces and provides an intuitive means for user-controlled balance between statistical efficiency and computational tractability. We illustrate the generic utility of our sampling algorithm through application to a range of statistical models. Supplementary materials for this article are available online.

Suggested Citation

  • Michalis K. Titsias & Christopher Yau, 2017. "The Hamming Ball Sampler," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(520), pages 1598-1611, October.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:520:p:1598-1611
    DOI: 10.1080/01621459.2016.1222288
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2016.1222288
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2016.1222288?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. Yanxun Xu & Peter Müller & Yuan Yuan & Kamalakar Gulukota & Yuan Ji, 2015. "MAD Bayes for Tumor Heterogeneity--Feature Allocation With Exponential Family Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 503-514, June.
    2. Tomi Peltola & Pekka Marttinen & Aki Vehtari, 2012. "Finite Adaptation and Multistep Moves in the Metropolis-Hastings Algorithm for Variable Selection in Genome-Wide Association Analysis," PLOS ONE, Public Library of Science, vol. 7(11), pages 1-11, November.
    3. Hans, Chris & Dobra, Adrian & West, Mike, 2007. "Shotgun Stochastic Search for," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 507-516, June.
    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. Quan Zhou & Jun Yang & Dootika Vats & Gareth O. Roberts & Jeffrey S. Rosenthal, 2022. "Dimension‐free mixing for high‐dimensional Bayesian variable selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 84(5), pages 1751-1784, November.

    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. Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
    2. Abdul Salam & Marco Grzegorczyk, 2023. "Model averaging for sparse seemingly unrelated regression using Bayesian networks among the errors," Computational Statistics, Springer, vol. 38(2), pages 779-808, June.
    3. Wang, Hao, 2010. "Sparse seemingly unrelated regression modelling: Applications in finance and econometrics," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2866-2877, November.
    4. Joscha Beckmann & Rainer Schüssler, 2014. "Forecasting Equity Premia using Bayesian Dynamic Model Averaging," CQE Working Papers 2914, Center for Quantitative Economics (CQE), University of Muenster.
    5. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2015. "Complete subset regressions with large-dimensional sets of predictors," Journal of Economic Dynamics and Control, Elsevier, vol. 54(C), pages 86-110.
    6. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    7. Athanassios Petralias & Pródromos Prodromídis, 2015. "Price discovery under crisis: uncovering the determinant factors of prices using efficient Bayesian model selection methods," Empirical Economics, Springer, vol. 49(3), pages 859-879, November.
    8. Kwon, Deukwoo & Landi, Maria Teresa & Vannucci, Marina & Issaq, Haleem J. & Prieto, DaRue & Pfeiffer, Ruth M., 2011. "An efficient stochastic search for Bayesian variable selection with high-dimensional correlated predictors," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2807-2818, October.
    9. P. Richard Hahn & Carlos M. Carvalho, 2015. "Decoupling Shrinkage and Selection in Bayesian Linear Models: A Posterior Summary Perspective," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 435-448, March.
    10. Lucas Joseph & Carvalho Carlos & West Mike, 2009. "A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-28, February.
    11. Elliott, Graham & Gargano, Antonio & Timmermann, Allan, 2013. "Complete subset regressions," Journal of Econometrics, Elsevier, vol. 177(2), pages 357-373.
    12. Kirsner, Daniel & Sansó, Bruno, 2020. "Multi-scale shotgun stochastic search for large spatial datasets," Computational Statistics & Data Analysis, Elsevier, vol. 146(C).
    13. Eicher, Theo S. & Helfman, Lindy & Lenkoski, Alex, 2012. "Robust FDI determinants: Bayesian Model Averaging in the presence of selection bias," Journal of Macroeconomics, Elsevier, vol. 34(3), pages 637-651.
    14. Nicolai Meinshausen & Peter Bühlmann, 2010. "Stability selection," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 417-473, September.
    15. Shiqiang Jin & Gyuhyeong Goh, 2021. "Bayesian selection of best subsets via hybrid search," Computational Statistics, Springer, vol. 36(3), pages 1991-2007, September.
    16. Li Ma, 2015. "Scalable Bayesian Model Averaging Through Local Information Propagation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 795-809, June.
    17. Fei Liu & David Dunson & Fei Zou, 2011. "High-Dimensional Variable Selection in Meta-Analysis for Censored Data," Biometrics, The International Biometric Society, vol. 67(2), pages 504-512, June.
    18. Liquet, Benoît & Bottolo, Leonardo & Campanella, Gianluca & Richardson, Sylvia & Chadeau-Hyam, Marc, 2016. "R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 69(i02).

    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:taf:jnlasa:v:112:y:2017:i:520:p:1598-1611. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.