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Preadjusted non-parametric estimation of a conditional distribution function

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F14%3A10218301" target="_blank" >RIV/00216208:11320/14:10218301 - isvavai.cz</a>

  • Result on the web

    <a href="http://onlinelibrary.wiley.com/doi/10.1111/rssb.12041/abstract" target="_blank" >http://onlinelibrary.wiley.com/doi/10.1111/rssb.12041/abstract</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1111/rssb.12041" target="_blank" >10.1111/rssb.12041</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Preadjusted non-parametric estimation of a conditional distribution function

  • Original language description

    The paper deals with non-parametric estimation of a conditional distribution function. We suggest a method of preadjusting the original observations non-parametrically through location and scale, to reduce the bias of the estimator. We derive the asymptotic properties of the estimator proposed. A simulation study investigating the finite sample performances of the estimators discussed is provided and reveals the gain that can be achieved. It is also shown how the idea of the preadjusting opens the pathto improved estimators in other settings such as conditional quantile and density estimation, and conditional survival function estimation in the case of censored data.

  • Czech name

  • Czech description

Classification

  • Type

    J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

  • CEP classification

    BA - General mathematics

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GPP201%2F11%2FP290" target="_blank" >GPP201/11/P290: Methods of statistical inference based on a distance matrix</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2014

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Name of the periodical

    Journal of the Royal Statistical Society. Series B: Statistical Methodology

  • ISSN

    1369-7412

  • e-ISSN

  • Volume of the periodical

    76

  • Issue of the periodical within the volume

    2

  • Country of publishing house

    GB - UNITED KINGDOM

  • Number of pages

    40

  • Pages from-to

    399-438

  • UT code for WoS article

    000331369500004

  • EID of the result in the Scopus database