IDEAS home Printed from https://ideas.repec.org/a/wly/apsmbi/v36y2020i6p1094-1110.html
   My bibliography  Save this article

Estimation of residential radon concentration in Pennsylvania counties by data fusion

Author

Listed:
  • Xuze Zhang
  • Saumyadipta Pyne
  • Benjamin Kedem

Abstract

A data fusion method for the estimation of residential radon level distribution in any Pennsylvania county is proposed. The method is based on a multisample density ratio model with variable tilts and is applied to combined radon data from a reference county of interest and its neighboring counties. Beaver county and its four immediate neighbors are taken as a case in point. The distribution of radon concentration is estimated in each of six periods, and then the analysis is repeated combining the data from all the periods to obtain estimates of Beaver threshold probabilities and the corresponding confidence intervals.

Suggested Citation

  • Xuze Zhang & Saumyadipta Pyne & Benjamin Kedem, 2020. "Estimation of residential radon concentration in Pennsylvania counties by data fusion," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 36(6), pages 1094-1110, November.
    • Handle: RePEc:wly:apsmbi:v:36:y:2020:i:6:p:1094-1110
    DOI: 10.1002/asmb.2546
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/asmb.2546
    Download Restriction: no
    File URL: https://libkey.io/10.1002/asmb.2546?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. Konstantinos Fokianos, 2004. "Merging information for semiparametric density estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 941-958, November.
    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. Zhang, Archer Gong & Chen, Jiahua, 2022. "Density ratio model with data-adaptive basis function," Journal of Multivariate Analysis, Elsevier, vol. 191(C).

    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. OrI Davidov & Konstantinos Fokianos & George Iliopoulos, 2014. "Semiparametric Inference for the Two-way Layout Under Order Restrictions," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(3), pages 622-638, September.
    2. Jiang, Shan & Tu, Dongsheng, 2012. "Inference on the probability P(T1," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1069-1078.
    3. Aubin, Jean-Baptiste & Leoni-Aubin, Samuela, 2008. "Projection density estimation under a m-sample semiparametric model," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2451-2468, January.
    4. Ori Davidov & Konstantinos Fokianos & George Iliopoulos, 2010. "Order-Restricted Semiparametric Inference for the Power Bias Model," Biometrics, The International Biometric Society, vol. 66(2), pages 549-557, June.
    5. Mohsen Arefi & Reinhard Viertl & S. Taheri, 2012. "Fuzzy density estimation," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(1), pages 5-22, January.
    6. Martin L. Hazelton & Berwin A. Turlach, 2010. "Semiparametric Density Deconvolution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 91-108, March.
    7. Zhang, Archer Gong & Chen, Jiahua, 2022. "Density ratio model with data-adaptive basis function," Journal of Multivariate Analysis, Elsevier, vol. 191(C).
    8. Yu-Min Huang, 2019. "Binary surrogates with stratified samples when weights are unknown," Computational Statistics, Springer, vol. 34(2), pages 653-682, 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:wly:apsmbi:v:36:y:2020:i:6:p:1094-1110. 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: https://doi.org/10.1002/(ISSN)1526-4025 .

    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.