IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v41y2014i9p1976-1988.html
   My bibliography  Save this article

Third-order likelihood-based inference for the log-normal regression model

Author

Listed:
  • Chwu-Shiun Tarng

Abstract

This paper examines the general third-order theory to the log-normal regression model. The interest parameter is its conditional mean. For inference, traditional first-order approximations need large sample sizes and normal-like distributions. Some specific third-order methods need the explicit forms of the nuisance parameter and ancillary statistic, which are quite complicated. Note that this general third-order theory can be applied to any continuous models with standard asymptotic properties. It only needs the log-likelihood function. With small sample settings, the simulation studies for confidence intervals of the conditional mean illustrate that the general third-order theory is much superior to the traditional first-order methods.

Suggested Citation

  • Chwu-Shiun Tarng, 2014. "Third-order likelihood-based inference for the log-normal regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(9), pages 1976-1988, September.
    • Handle: RePEc:taf:japsta:v:41:y:2014:i:9:p:1976-1988
    DOI: 10.1080/02664763.2014.898134
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2014.898134
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2014.898134?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.

    References listed on IDEAS

    as
    1. Rekkas, M. & Wong, A., 2005. "Third-order inference for the Weibull distribution," Computational Statistics & Data Analysis, Elsevier, vol. 49(2), pages 499-525, April.
    2. Fraser, D.A.S. & Rekkas, M. & Wong, A., 2005. "Highly accurate likelihood analysis for the seemingly unrelated regression problem," Journal of Econometrics, Elsevier, vol. 127(1), pages 17-33, July.
    Full references (including those not matched with items on IDEAS)

    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. Zellner, Arnold & Ando, Tomohiro, 2010. "A direct Monte Carlo approach for Bayesian analysis of the seemingly unrelated regression model," Journal of Econometrics, Elsevier, vol. 159(1), pages 33-45, November.
    2. Meuer, Johannes & Rupietta, Christian & Backes-Gellner, Uschi, 2015. "Layers of co-existing innovation systems," Research Policy, Elsevier, vol. 44(4), pages 888-910.
    3. Tiong, Kah Yong & Ma, Zhenliang & Palmqvist, Carl-William, 2023. "Analyzing factors contributing to real-time train arrival delays using seemingly unrelated regression models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 174(C).
    4. Zhao, Li & Xu, Xingzhong, 2017. "Generalized canonical correlation variables improved estimation in high dimensional seemingly unrelated regression models," Statistics & Probability Letters, Elsevier, vol. 126(C), pages 119-126.
    5. Wang, Min & Sun, Xiaoqian, 2012. "Bayesian inference for the correlation coefficient in two seemingly unrelated regressions," Computational Statistics & Data Analysis, Elsevier, vol. 56(8), pages 2442-2453.
    6. Zellner, Arnold & Ando, Tomohiro, 2010. "Bayesian and non-Bayesian analysis of the seemingly unrelated regression model with Student-t errors, and its application for forecasting," International Journal of Forecasting, Elsevier, vol. 26(2), pages 413-434, April.

    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:japsta:v:41:y:2014:i:9:p:1976-1988. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.