IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v112y2017i519p1156-1168.html
   My bibliography  Save this article

Generalized Scalar-on-Image Regression Models via Total Variation

Author

Listed:
  • Xiao Wang
  • Hongtu Zhu

Abstract

The use of imaging markers to predict clinical outcomes can have a great impact in public health. The aim of this article is to develop a class of generalized scalar-on-image regression models via total variation (GSIRM-TV), in the sense of generalized linear models, for scalar response and imaging predictor with the presence of scalar covariates. A key novelty of GSIRM-TV is that it is assumed that the slope function (or image) of GSIRM-TV belongs to the space of bounded total variation to explicitly account for the piecewise smooth nature of most imaging data. We develop an efficient penalized total variation optimization to estimate the unknown slope function and other parameters. We also establish nonasymptotic error bounds on the excess risk. These bounds are explicitly specified in terms of sample size, image size, and image smoothness. Our simulations demonstrate a superior performance of GSIRM-TV against many existing approaches. We apply GSIRM-TV to the analysis of hippocampus data obtained from the Alzheimers Disease Neuroimaging Initiative (ADNI) dataset. Supplementary materials for this article are available online.

Suggested Citation

  • Xiao Wang & Hongtu Zhu, 2017. "Generalized Scalar-on-Image Regression Models via Total Variation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1156-1168, July.
    • Handle: RePEc:taf:jnlasa:v:112:y:2017:i:519:p:1156-1168
    DOI: 10.1080/01621459.2016.1194846
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2016.1194846
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/01621459.2016.1194846?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. Wei Hu & Tianyu Pan & Dehan Kong & Weining Shen, 2021. "Nonparametric matrix response regression with application to brain imaging data analysis," Biometrics, The International Biometric Society, vol. 77(4), pages 1227-1240, December.
    2. Weicheng Zhu & Sheng Xu & Catherine C. Liu & Yehua Li, 2023. "Minimax powerful functional analysis of covariance tests with application to longitudinal genome‐wide association studies," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 50(1), pages 266-295, March.
    3. Cui Guo & Jian Kang & Timothy D. Johnson, 2022. "A spatial Bayesian latent factor model for image‐on‐image regression," Biometrics, The International Biometric Society, vol. 78(1), pages 72-84, March.
    4. Baiguo An & Beibei Zhang, 2020. "Logistic regression with image covariates via the combination of L1 and Sobolev regularizations," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-18, June.
    5. Xu Gao & Weining Shen & Liwen Zhang & Jianhua Hu & Norbert J. Fortin & Ron D. Frostig & Hernando Ombao, 2021. "Regularized matrix data clustering and its application to image analysis," Biometrics, The International Biometric Society, vol. 77(3), pages 890-902, September.
    6. Li, Yehua & Qiu, Yumou & Xu, Yuhang, 2022. "From multivariate to functional data analysis: Fundamentals, recent developments, and emerging areas," Journal of Multivariate Analysis, Elsevier, vol. 188(C).
    7. Wang, Chuchu & Song, Xinyuan, 2024. "Nonparametric quantile scalar-on-image regression," Computational Statistics & Data Analysis, Elsevier, vol. 191(C).
    8. Yize Zhao & Ben Wu & Jian Kang, 2023. "Bayesian interaction selection model for multimodal neuroimaging data analysis," Biometrics, The International Biometric Society, vol. 79(2), pages 655-668, June.

    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:519:p:1156-1168. 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.