n. Supplementary materials for this article are available online."> n. Supplementary materials for this article are available online.">
[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v113y2018i521p431-444.html
   My bibliography  Save this article

The Spike-and-Slab LASSO

Author

Listed:
  • Veronika Ročková
  • Edward I. George
Abstract
Despite the wide adoption of spike-and-slab methodology for Bayesian variable selection, its potential for penalized likelihood estimation has largely been overlooked. In this article, we bridge this gap by cross-fertilizing these two paradigms with the Spike-and-Slab LASSO procedure for variable selection and parameter estimation in linear regression. We introduce a new class of self-adaptive penalty functions that arise from a fully Bayes spike-and-slab formulation, ultimately moving beyond the separable penalty framework. A virtue of these nonseparable penalties is their ability to borrow strength across coordinates, adapt to ensemble sparsity information and exert multiplicity adjustment. The Spike-and-Slab LASSO procedure harvests efficient coordinate-wise implementations with a path-following scheme for dynamic posterior exploration. We show on simulated data that the fully Bayes penalty mimics oracle performance, providing a viable alternative to cross-validation. We develop theory for the separable and nonseparable variants of the penalty, showing rate-optimality of the global mode as well as optimal posterior concentration when p > n. Supplementary materials for this article are available online.

Suggested Citation

  • Veronika Ročková & Edward I. George, 2018. "The Spike-and-Slab LASSO," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(521), pages 431-444, January.
    • Handle: RePEc:taf:jnlasa:v:113:y:2018:i:521:p:431-444
    DOI: 10.1080/01621459.2016.1260469
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2016.1260469
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2016.1260469?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Sierra A. Bainter & Thomas G. McCauley & Mahmoud M. Fahmy & Zachary T. Goodman & Lauren B. Kupis & J. Sunil Rao, 2023. "Comparing Bayesian Variable Selection to Lasso Approaches for Applications in Psychology," Psychometrika, Springer;The Psychometric Society, vol. 88(3), pages 1032-1055, September.
    2. Lee, Juyong & Reiner, David M., 2023. "Determinants of public preferences on low-carbon energy sources: Evidence from the United Kingdom," Energy, Elsevier, vol. 284(C).
    3. Yinghan Chen & Steven Andrew Culpepper & Yuguo Chen, 2023. "Bayesian Inference for an Unknown Number of Attributes in Restricted Latent Class Models," Psychometrika, Springer;The Psychometric Society, vol. 88(2), pages 613-635, June.
    4. Mogliani, Matteo & Simoni, Anna, 2021. "Bayesian MIDAS penalized regressions: Estimation, selection, and prediction," Journal of Econometrics, Elsevier, vol. 222(1), pages 833-860.
    5. M. Marsman & K. Huth & L. J. Waldorp & I. Ntzoufras, 2022. "Objective Bayesian Edge Screening and Structure Selection for Ising Networks," Psychometrika, Springer;The Psychometric Society, vol. 87(1), pages 47-82, March.
    6. Byrd, Michael & Nghiem, Linh H. & McGee, Monnie, 2021. "Bayesian regularization of Gaussian graphical models with measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    7. Niesert, Robin F. & Oorschot, Jochem A. & Veldhuisen, Christian P. & Brons, Kester & Lange, Rutger-Jan, 2020. "Can Google search data help predict macroeconomic series?," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1163-1172.
    8. Wang, Xiaoqing & Feng, Xiangnan & Song, Xinyuan, 2020. "Joint analysis of semicontinuous data with latent variables," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    9. Parley R Yang & Alexander Y Shestopaloff, 2024. "Low Volatility Stock Portfolio Through High Dimensional Bayesian Cointegration," Papers 2407.10175, arXiv.org.
    10. Posch, Konstantin & Arbeiter, Maximilian & Pilz, Juergen, 2020. "A novel Bayesian approach for variable selection in linear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    11. Banerjee, Sayantan, 2022. "Horseshoe shrinkage methods for Bayesian fusion estimation," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    12. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    13. Zhang, Ruoyang & Ghosh, Malay, 2022. "Ultra high-dimensional multivariate posterior contraction rate under shrinkage priors," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    14. 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.
    15. Tanin Sirimongkolkasem & Reza Drikvandi, 2019. "On Regularisation Methods for Analysis of High Dimensional Data," Annals of Data Science, Springer, vol. 6(4), pages 737-763, December.
    16. Minerva Mukhopadhyay & David B. Dunson, 2020. "Targeted Random Projection for Prediction From High-Dimensional Features," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(532), pages 1998-2010, December.
    17. Hu, Guanyu, 2021. "Spatially varying sparsity in dynamic regression models," Econometrics and Statistics, Elsevier, vol. 17(C), pages 23-34.
    18. Pereira, Luz Adriana & Gutiérrez, Luis & Taylor-Rodríguez, Daniel & Mena, Ramsés H., 2023. "Bayesian nonparametric hypothesis testing for longitudinal data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 179(C).
    19. Gonzalo García-Donato & María Eugenia Castellanos & Alicia Quirós, 2021. "Bayesian Variable Selection with Applications in Health Sciences," Mathematics, MDPI, vol. 9(3), pages 1-16, January.
    20. Okudo, Michiko & Komaki, Fumiyasu, 2021. "Shrinkage priors for single-spiked covariance models," Statistics & Probability Letters, Elsevier, vol. 176(C).
    21. Posch, Konstantin & Truden, Christian & Hungerländer, Philipp & Pilz, Jürgen, 2022. "A Bayesian approach for predicting food and beverage sales in staff canteens and restaurants," International Journal of Forecasting, Elsevier, vol. 38(1), pages 321-338.
    22. Richard K. Crump & Nikolay Gospodinov & Hunter Wieman, 2023. "Sparse Trend Estimation," Staff Reports 1049, Federal Reserve Bank of New York.
    23. Kshitij Khare & Malay Ghosh, 2022. "MCMC Convergence for Global-Local Shrinkage Priors," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 20(1), pages 211-234, 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:taf:jnlasa:v:113:y:2018:i:521:p:431-444. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.