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Outlier detection by means of robust regression estimators for use in engineering science

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F09%3A10104241" target="_blank" >RIV/00216208:11320/09:10104241 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1631/jzus.A0820140" target="_blank" >http://dx.doi.org/10.1631/jzus.A0820140</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1631/jzus.A0820140" target="_blank" >10.1631/jzus.A0820140</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Outlier detection by means of robust regression estimators for use in engineering science

  • Original language description

    The paper compares the ability of different robust regression estimators to detect and classify outliers. Estimators with a high breakdown point are compared and conclusions are drawn for real engineering applications. The least trimmed squares estimatoris recommended for heavily contaminated data sets with outliers with a complicated multivariate structure.

  • Czech name

  • Czech description

Classification

  • Type

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

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2009

  • 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 Zhejiang University: Science A

  • ISSN

    1673-565X

  • e-ISSN

  • Volume of the periodical

    10

  • Issue of the periodical within the volume

    6

  • Country of publishing house

    CN - CHINA

  • Number of pages

    13

  • Pages from-to

    909-921

  • UT code for WoS article

    000266497300017

  • EID of the result in the Scopus database