IDEAS home Printed from https://ideas.repec.org/a/bla/stanee/v69y2015i3p236-271.html
   My bibliography  Save this article

Bias reduction when data are rounded

Author

Listed:
  • Christopher S. Withers
  • Saralees Nadarajah

Abstract

type="main" xml:id="stan12057-abs-0001"> Analytical bias reduction methods are developed for univariate rounded data for the first time. Extensions are given to rounding of multivariate data, and to smooth functionals of several distributions. As a by-product, we give for the first time the relation between rounded and unrounded multivariate cumulants. Estimators obtained by analytical bias reduction are compared with bootstrap and jackknife estimators by simulation.

Suggested Citation

  • Christopher S. Withers & Saralees Nadarajah, 2015. "Bias reduction when data are rounded," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 236-271, August.
    • Handle: RePEc:bla:stanee:v:69:y:2015:i:3:p:236-271
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/stan.12057
    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.

    References listed on IDEAS

    as
    1. Wang, B. & Wertelecki, W., 2013. "Density estimation for data with rounding errors," Computational Statistics & Data Analysis, Elsevier, vol. 65(C), pages 4-12.
    2. Weiming Li & Z. D. Bai, 2011. "Analysis of accumulated rounding errors in autoregressive processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 32(5), pages 518-530, September.
    3. Weiming Li & Tianqing Liu & Zhidong Bai, 2012. "Rounded data analysis based on ranked set sample," Statistical Papers, Springer, vol. 53(2), pages 439-455, May.
    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. Christopher Withers & Saralees Nadarajah, 2015. "Cumulants of a random variable distributed uniformly on the first $$n$$ n integers," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 99(2), pages 229-236, April.
    2. Marcus Groß & Ulrich Rendtel & Timo Schmid & Sebastian Schmon & Nikos Tzavidis, 2017. "Estimating the density of ethnic minorities and aged people in Berlin: multivariate kernel density estimation applied to sensitive georeferenced administrative data protected via measurement error," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 161-183, January.
    3. Groß, Marcus & Rendtel, Ulrich & Schmid, Timo & Schmon, Sebastian & Tzavidis, Nikos, 2015. "Estimating the density of ethnic minorities and aged people in Berlin: Multivariate kernel density estimation applied to sensitive geo-referenced administrative data protected via measurement error," Discussion Papers 2015/7, Free University Berlin, School of Business & Economics.
    4. Jesse Frey & Timothy G. Feeman, 2017. "Efficiency comparisons for partially rank-ordered set sampling," Statistical Papers, Springer, vol. 58(4), pages 1149-1163, December.
    5. Walter, Paul & Weimer, Katja, 2018. "Estimating poverty and inequality indicators using interval censored income data from the German microcensus," Discussion Papers 2018/10, Free University Berlin, School of Business & Economics.
    6. Francisco Blasques & Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Zero-Inflated Autoregressive Conditional Duration Model for Discrete Trade Durations with Excessive Zeros," Papers 1812.07318, arXiv.org, revised Jan 2022.
    7. Ehsan Zamanzade & Michael Vock, 2018. "Some nonparametric tests of perfect judgment ranking for judgment post stratification," Statistical Papers, Springer, vol. 59(3), pages 1085-1100, September.
    8. Bermúdez, Lluís & Karlis, Dimitris & Santolino, Miguel, 2017. "A finite mixture of multiple discrete distributions for modelling heaped count data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 14-23.

    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:stanee:v:69:y:2015:i:3:p:236-271. 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: http://www.blackwellpublishing.com/journal.asp?ref=0039-0402 .

    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.