IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v78y2022i4p1328-1341.html
   My bibliography  Save this article

Varying‐coefficient regression analysis for pooled biomonitoring

Author

Listed:
  • Dewei Wang
  • Xichen Mou
  • Yan Liu

Abstract

Human biomonitoring involves measuring the accumulation of contaminants in biological specimens (such as blood or urine) to assess individuals' exposure to environmental contamination. Due to the expensive cost of a single assay, the method of pooling has become increasingly common in environmental studies. The implementation of pooling starts by physically mixing specimens into pools, and then measures pooled specimens for the concentration of contaminants. An important task is to reconstruct individual‐level statistical characteristics based on pooled measurements. In this article, we propose to use the varying‐coefficient regression model for individual‐level biomonitoring and provide methods to estimate the varying coefficients based on different types of pooled data. Asymptotic properties of the estimators are presented. We illustrate our methodology via simulation and with application to pooled biomonitoring of a brominated flame retardant provided by the National Health and Nutrition Examination Survey (NHANES).

Suggested Citation

  • Dewei Wang & Xichen Mou & Yan Liu, 2022. "Varying‐coefficient regression analysis for pooled biomonitoring," Biometrics, The International Biometric Society, vol. 78(4), pages 1328-1341, December.
    • Handle: RePEc:bla:biomet:v:78:y:2022:i:4:p:1328-1341
    DOI: 10.1111/biom.13516
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/biom.13516
    Download Restriction: no
    File URL: https://libkey.io/10.1111/biom.13516?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
    ---><---

    References listed on IDEAS

    as
    1. S. Vansteelandt & E. Goetghebeur & T. Verstraeten, 2000. "Regression Models for Disease Prevalence with Diagnostic Tests on Pools of Serum Samples," Biometrics, The International Biometric Society, vol. 56(4), pages 1126-1133, December.
    2. Wang, Hansheng & Xia, Yingcun, 2009. "Shrinkage Estimation of the Varying Coefficient Model," Journal of the American Statistical Association, American Statistical Association, vol. 104(486), pages 747-757.
    3. Emily M. Mitchell & Robert H. Lyles & Amita K. Manatunga & Michelle Danaher & Neil J. Perkins & Enrique F. Schisterman, 2014. "Regression for skewed biomarker outcomes subject to pooling," Biometrics, The International Biometric Society, vol. 70(1), pages 202-211, March.
    4. Juexin Lin & Dewei Wang, 2018. "Single-index regression for pooled biomarker data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(4), pages 813-833, October.
    5. Jingru Mu & Guannan Wang & Li Wang, 2018. "Estimation and inference in spatially varying coefficient models," Environmetrics, John Wiley & Sons, Ltd., vol. 29(1), February.
    6. Yaakov Malinovsky & Paul S. Albert & Enrique F. Schisterman, 2012. "Pooling Designs for Outcomes under a Gaussian Random Effects Model," Biometrics, The International Biometric Society, vol. 68(1), pages 45-52, March.
    7. Dewei Wang & Xichen Mou & Xiang Li & Xianzheng Huang, 2020. "Local polynomial regression for pooled response data," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 32(4), pages 814-837, October.
    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. Luis A. Barboza & Shu Wei Chou Chen & Marcela Alfaro Córdoba & Eric J. Alfaro & Hugo G. Hidalgo, 2023. "Spatio‐temporal downscaling emulator for regional climate models," Environmetrics, John Wiley & Sons, Ltd., vol. 34(7), 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. Mou, Xichen & Wang, Dewei, 2024. "Additive partially linear model for pooled biomonitoring data," Computational Statistics & Data Analysis, Elsevier, vol. 190(C).
    2. Emily M. Mitchell & Robert H. Lyles & Amita K. Manatunga & Michelle Danaher & Neil J. Perkins & Enrique F. Schisterman, 2014. "Regression for skewed biomarker outcomes subject to pooling," Biometrics, The International Biometric Society, vol. 70(1), pages 202-211, March.
    3. Jun Jin & Tiefeng Ma & Jiajia Dai, 2021. "New efficient spline estimation for varying-coefficient models with two-step knot number selection," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 693-712, July.
    4. Wang, Dewei & McMahan, Christopher S. & Tebbs, Joshua M. & Bilder, Christopher R., 2018. "Group testing case identification with biomarker information," Computational Statistics & Data Analysis, Elsevier, vol. 122(C), pages 156-166.
    5. Wongsa-art, Pipat & Kim, Namhyun & Xia, Yingcun & Moscone, Francesco, 2024. "Varying coefficient panel data models and methods under correlated error components: Application to disparities in mental health services in England," Regional Science and Urban Economics, Elsevier, vol. 106(C).
    6. Gaorong Li & Liugen Xue & Heng Lian, 2012. "SCAD-penalised generalised additive models with non-polynomial dimensionality," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(3), pages 681-697.
    7. Zhang, Tao & Zhang, Qingzhao & Wang, Qihua, 2014. "Model detection for functional polynomial regression," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 183-197.
    8. Tang, Yanlin & Song, Xinyuan & Wang, Huixia Judy & Zhu, Zhongyi, 2013. "Variable selection in high-dimensional quantile varying coefficient models," Journal of Multivariate Analysis, Elsevier, vol. 122(C), pages 115-132.
    9. Feng, Guohua & Gao, Jiti & Peng, Bin, 2022. "An integrated panel data approach to modelling economic growth," Journal of Econometrics, Elsevier, vol. 228(2), pages 379-397.
    10. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    11. Jingyuan Liu & Runze Li & Rongling Wu, 2014. "Feature Selection for Varying Coefficient Models With Ultrahigh-Dimensional Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 266-274, March.
    12. Diego Vidaurre & Concha Bielza & Pedro Larrañaga, 2012. "Lazy lasso for local regression," Computational Statistics, Springer, vol. 27(3), pages 531-550, September.
    13. Yongjin Li & Qingzhao Zhang & Qihua Wang, 2017. "Penalized estimation equation for an extended single-index model," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 169-187, February.
    14. Chen, Bin & Maung, Kenwin, 2023. "Time-varying forecast combination for high-dimensional data," Journal of Econometrics, Elsevier, vol. 237(2).
    15. Lu, Jun & Lin, Lu, 2018. "Feature screening for multi-response varying coefficient models with ultrahigh dimensional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 242-254.
    16. Feng, Long & Zou, Changliang & Wang, Zhaojun, 2012. "Local Walsh-average regression," Journal of Multivariate Analysis, Elsevier, vol. 106(C), pages 36-48.
    17. Fang Lu & Jing Yang & Xuewen Lu, 2022. "One-step oracle procedure for semi-parametric spatial autoregressive model and its empirical application to Boston housing price data," Empirical Economics, Springer, vol. 62(6), pages 2645-2671, June.
    18. Qiu, Jia & Li, Degao & You, Jinhong, 2015. "SCAD-penalized regression for varying-coefficient models with autoregressive errors," Journal of Multivariate Analysis, Elsevier, vol. 137(C), pages 100-118.
    19. Qu, Lianqiang & Song, Xinyuan & Sun, Liuquan, 2018. "Identification of local sparsity and variable selection for varying coefficient additive hazards models," Computational Statistics & Data Analysis, Elsevier, vol. 125(C), pages 119-135.
    20. Abbas Khalili & Farhad Shokoohi & Masoud Asgharian & Shili Lin, 2023. "Sparse estimation in semiparametric finite mixture of varying coefficient regression models," Biometrics, The International Biometric Society, vol. 79(4), pages 3445-3457, December.

    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:bla:biomet:v:78:y:2022:i:4:p:1328-1341. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0006-341X .

    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.