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On Robust Testing for Normality of Error Terms in Regression Models

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F15%3A43906564" target="_blank" >RIV/62156489:43110/15:43906564 - isvavai.cz</a>

  • Result on the web

    <a href="http://mme2015.zcu.cz/downloads/MME_2015_proceedings.pdf" target="_blank" >http://mme2015.zcu.cz/downloads/MME_2015_proceedings.pdf</a>

  • DOI - Digital Object Identifier

Alternative languages

  • Result language

    angličtina

  • Original language name

    On Robust Testing for Normality of Error Terms in Regression Models

  • Original language description

    Testing for normality of error terms constitutes one of the most important steps of regression model verification and validation, because failure to assess non-normality of the regression residuals may lead to incorrect results since significant deviations from normality can substantially affect the performance of usual statistical inference techniques. Thus, in the majority of cases of relevant regression analysis normality of error terms is expected. However, this is not true in many practical situations. While OLS estimator is known to be very sensitive to outliers, the robust regression estimator (e.g. Least Trimmed Squares, LTS) is known not to be unduly affected by the presence of outliers. The aim of this paper is to present and discuss the trade-off between power and robustness of selected classical and robust normality tests of error terms in regression models. For this purpose we use OLS and LTS residuals from linear regression models with various distributed dependent variab

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    BB - Applied statistics, operational research

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2015

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    Mathematical Methods in Economics 2015: Conference Proceedings

  • ISBN

    978-80-261-0539-8

  • ISSN

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

    749-754

  • Publisher name

    Západočeská univerzita

  • Place of publication

    Plzeň

  • Event location

    Cheb

  • Event date

    Sep 9, 2015

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article