IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v92y2005i3p543-557.html
   My bibliography  Save this article

Smooth quantile ratio estimation

Author

Listed:
  • Francesca Dominici
  • Leslie Cope
  • Daniel Q. Naiman
  • Scott L. Zeger

Abstract

We propose a novel approach to estimating the mean difference between two highly skewed distributions. The method, which we call smooth quantile ratio estimation, smooths, over percentiles, the ratio of the quantiles of the two distributions. The method defines a large class of estimators, including the sample mean difference, the maximum likelihood estimator under log-normal samples and the L-estimator. We derive asymptotic properties such as consistency and asymptotic normality, and also provide a closed-form expression for the asymptotic variance. In a simulation study, we show that smooth quantile ratio estimation has lower mean squared error than several competitors, including the sample mean difference and the log-normal parametric estimator in several realistic situations. We apply the method to the 1987 National Medicare Expenditure Survey to estimate the difference in medical expenditures between persons suffering from the smoking attributable diseases, lung cancer and chronic obstructive pulmonary disease, and persons without these diseases. Copyright 2005, Oxford University Press.

Suggested Citation

  • Francesca Dominici & Leslie Cope & Daniel Q. Naiman & Scott L. Zeger, 2005. "Smooth quantile ratio estimation," Biometrika, Biometrika Trust, vol. 92(3), pages 543-557, September.
  • Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:543-557
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/92.3.543
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Fabian Dunker & Stephan Klasen & Tatyana Krivobokova, 2017. "Asymptotic Distribution and Simultaneous Confidence Bands for Ratios of Quantile Functions," Papers 1710.09009, arXiv.org.
    2. P. B. Kenfac Dongmezo & P. N. Mwita & I. R. Kamga Tchwaket, 2017. "Imputation Based Treatment Effect Estimators," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 6(3), pages 1-2.
    3. Dandan Xu & Michael J. Daniels & Almut G. Winterstein, 2018. "A Bayesian nonparametric approach to causal inference on quantiles," Biometrics, The International Biometric Society, vol. 74(3), pages 986-996, September.
    4. Borislava Mihaylova & Andrew Briggs & Anthony O'Hagan & Simon G. Thompson, 2011. "Review of statistical methods for analysing healthcare resources and costs," Health Economics, John Wiley & Sons, Ltd., vol. 20(8), pages 897-916, 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:oup:biomet:v:92:y:2005:i:3:p:543-557. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.