IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v33y2006i1p153-168.html
   My bibliography  Save this article

Empirical Likelihood Confidence Region for Parameters in Semi‐linear Errors‐in‐Variables Models

Author

Listed:
  • HENGJIAN CUI
  • EFANG KONG

Abstract

. This paper proposes a constrained empirical likelihood confidence region for a parameter in the semi‐linear errors‐in‐variables model. The confidence region is constructed by combining the score function corresponding to the squared orthogonal distance with a constraint on the parameter, and it overcomes that the solution of limiting mean estimation equations is not unique. It is shown that the empirical log likelihood ratio at the true parameter converges to the standard chi‐square distribution. Simulations show that the proposed confidence region has coverage probability which is closer to the nominal level, as well as narrower than those of normal approximation of generalized least squares estimator in most cases. A real data example is given.

Suggested Citation

  • Hengjian Cui & Efang Kong, 2006. "Empirical Likelihood Confidence Region for Parameters in Semi‐linear Errors‐in‐Variables Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(1), pages 153-168, March.
    • Handle: RePEc:bla:scjsta:v:33:y:2006:i:1:p:153-168
    DOI: 10.1111/j.1467-9469.2006.00468.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-9469.2006.00468.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-9469.2006.00468.x?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
    ---><---

    Citations

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


    Cited by:

    1. Jianhong Shi & Fanrong Zhao, 2018. "Statistical inference for heteroscedastic semi-varying coefficient EV models under restricted condition," Statistical Papers, Springer, vol. 59(2), pages 487-511, June.
    2. Zhang, Junhua & Feng, Sanying & Li, Gaorong & Lian, Heng, 2011. "Empirical likelihood inference for partially linear panel data models with fixed effects," Economics Letters, Elsevier, vol. 113(2), pages 165-167.
    3. Tang, Linjun & Zhou, Zhangong & Wu, Changchun, 2013. "Testing the linear errors-in-variables model with randomly censored data," Statistics & Probability Letters, Elsevier, vol. 83(3), pages 875-884.
    4. Guo-Liang Fan & Han-Ying Liang & Jiang-Feng Wang, 2013. "Empirical likelihood for heteroscedastic partially linear errors-in-variables model with α-mixing errors," Statistical Papers, Springer, vol. 54(1), pages 85-112, February.
    5. Yan, Li & Chen, Xia, 2014. "Empirical likelihood for partly linear models with errors in all variables," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 275-288.
    6. Sun, Zhimeng & Zhang, Zhongzhan, 2013. "Semiparametric analysis of additive isotonic errors-in-variables regression models," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 100-114.
    7. Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    8. Bianco, Ana M. & Spano, Paula M., 2017. "Robust estimation in partially linear errors-in-variables models," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 46-64.
    9. Cui, Hengjian & Hu, Tao, 2011. "On nonlinear regression estimator with denoised variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1137-1149, February.
    10. Huang, Zhensheng, 2012. "Empirical likelihood for the parametric part in partially linear errors-in-function models," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 63-66.
    11. Xia Chen & Liyue Mao, 2020. "Penalized empirical likelihood for partially linear errors-in-variables models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 104(4), pages 597-623, December.
    12. Amjad D. Al-Nasser, 2014. "Two steps generalized maximum entropy estimation procedure for fitting linear regression when both covariates are subject to error," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(8), pages 1708-1720, August.

    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:scjsta:v:33:y:2006:i:1:p:153-168. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.