IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v91y2014icp69-75.html
   My bibliography  Save this article

Shorter nonparametric prediction intervals for an order statistic from a future sample

Author

Listed:
  • Frey, Jesse

Abstract

Standard nonparametric prediction intervals for a future order statistic are obtained by taking the interval between two order statistics of the initial sample. We obtain improved prediction intervals by taking the shortest of two or more standard intervals.

Suggested Citation

  • Frey, Jesse, 2014. "Shorter nonparametric prediction intervals for an order statistic from a future sample," Statistics & Probability Letters, Elsevier, vol. 91(C), pages 69-75.
  • Handle: RePEc:eee:stapro:v:91:y:2014:i:c:p:69-75
    DOI: 10.1016/j.spl.2014.04.011
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715214001424
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.spl.2014.04.011?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. Hettmansperger, Thomas P. & Sheather, Simon J., 1986. "Confidence intervals based on interpolated order statistics," Statistics & Probability Letters, Elsevier, vol. 4(2), pages 75-79, March.
    2. Di Bucchianico, A. & Einmahl, J.H.J. & Mushkudiani, N.A., 2001. "Smallest nonparametric tolerance regions," Other publications TiSEM 436f9be2-d0ad-49af-b6df-9, Tilburg University, School of Economics and Management.
    3. Jesse Frey, 2010. "Data-driven nonparametric tolerance sets," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 169-180.
    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. Kedai Cheng & Derek S. Young, 2023. "An Approach for Specifying Trimming and Winsorization Cutoffs," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 28(2), pages 299-323, June.
    2. Ilaria Lucrezia Amerise, 2023. "A direct method for constructing distribution-free tolerance regions," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(5), pages 3941-3954, October.
    3. Jesse Frey & Yimin Zhang, 2017. "What Do Interpolated Nonparametric Confidence Intervals for Population Quantiles Guarantee?," The American Statistician, Taylor & Francis Journals, vol. 71(4), pages 305-309, October.
    4. Olive, David J., 2007. "Prediction intervals for regression models," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3115-3122, March.
    5. Beutner, E. & Cramer, E., 2014. "Using linear interpolation to reduce the order of the coverage error of nonparametric prediction intervals based on right-censored data," Journal of Multivariate Analysis, Elsevier, vol. 129(C), pages 95-109.
    6. Florens, Jean-Pierre & Simar, Léopold & Van Keilegom, Ingrid, 2014. "Frontier estimation in nonparametric location-scale models," Journal of Econometrics, Elsevier, vol. 178(P3), pages 456-470.
    7. David J. Olive, 2018. "Applications of hyperellipsoidal prediction regions," Statistical Papers, Springer, vol. 59(3), pages 913-931, September.
    8. Amparo Baíllo & Antonio Cuevas, 2006. "Parametric versus nonparametric tolerance regions in detection problems," Computational Statistics, Springer, vol. 21(3), pages 523-536, December.
    9. Szilárd Nemes, 2019. "Likelihood Confidence Intervals When Only Ranges Are Available," Stats, MDPI, vol. 2(1), pages 1-7, February.
    10. Chaitra H. Nagaraja & Haikady N. Nagaraja, 2020. "Distribution‐free Approximate Methods for Constructing Confidence Intervals for Quantiles," International Statistical Review, International Statistical Institute, vol. 88(1), pages 75-100, April.
    11. Jesse Frey, 2010. "Data-driven nonparametric tolerance sets," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 169-180.
    12. Bell, Peter N, 2013. "New Testing Procedures to Assess Market Efficiency with Trading Rules," MPRA Paper 46701, University Library of Munich, Germany.
    13. Serfling, Robert, 2002. "Generalized Quantile Processes Based on Multivariate Depth Functions, with Applications in Nonparametric Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 83(1), pages 232-247, October.
    14. Nourmohammadi, Mohammad & Jafari Jozani, Mohammad & Johnson, Brad C., 2014. "Confidence intervals for quantiles in finite populations with randomized nomination sampling," Computational Statistics & Data Analysis, Elsevier, vol. 73(C), pages 112-128.
    15. Juan Herrero & Carmen Castañeda, 2023. "Comparing Two Saline-Gypseous Wetland Soils in NE Spain," Land, MDPI, vol. 12(11), pages 1-17, October.
    16. Papadatos, N., 1995. "Intermediate order statistics with applications to nonparametric estimation," Statistics & Probability Letters, Elsevier, vol. 22(3), pages 231-238, February.
    17. Algo Carè & Simone Garatti & Marco C. Campi, 2017. "A coverage theory for least squares," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(5), pages 1367-1389, November.
    18. C. Tsao & Yu-Ling Tseng, 2006. "Confidence estimation for tolerance intervals," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(3), pages 441-456, September.

    More about this item

    Keywords

    Beta distribution; Sample median;

    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:eee:stapro:v:91:y:2014:i:c:p:69-75. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.