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A Robustified Metalearning Procedure for Regression Estimators

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F19%3A00510771" target="_blank" >RIV/67985556:_____/19:00510771 - isvavai.cz</a>

  • Alternative codes found

    RIV/67985807:_____/19:00510554

  • Result on the web

    <a href="http://dx.doi.org/10.18267/pr.2019.los.186.61" target="_blank" >http://dx.doi.org/10.18267/pr.2019.los.186.61</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.18267/pr.2019.los.186.61" target="_blank" >10.18267/pr.2019.los.186.61</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    A Robustified Metalearning Procedure for Regression Estimators

  • Original language description

    Metalearning represents a useful methodology for selecting and recommending a suitable algorithm or method for a new dataset exploiting a database of training datasets. While metalearning is potentially beneficial for the analysis of economic data, we must be aware of its instability and sensitivity to outlying measurements (outliers) as well as measurement errors. The aim of this paper is to robustify the metalearning process. First, we prepare some useful theoretical tools exploiting the idea of implicit weighting, inspired by the least weighted squares estimator. These include a robust coefficient of determination, a robust version of mean square error, and a simple rule for outlier detection in linear regression. We perform a metalearning study for recommending the best linear regression estimator for a new dataset (not included in the training database). The prediction of the optimal estimator is learned over a set of 20 real datasets with economic motivation, while the least squares are compared with several (highly) robust estimators. We investigate the effect of variable selection on the metalearning results. If the training as well as validation data are considered after a proper robust variable selection, the metalearning performance is improved remarkably, especially if a robust prediction error is used.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10101 - Pure mathematics

Result continuities

  • Project

    <a href="/en/project/GA17-07384S" target="_blank" >GA17-07384S: Nonparametric (statistical) methods in modern econometrics</a><br>

  • Continuities

    I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Others

  • Publication year

    2019

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    The 13th International Days of Statistics and Economics Conference Proceedings

  • ISBN

    978-80-87990-18-6

  • ISSN

  • e-ISSN

  • Number of pages

    10

  • Pages from-to

    617-626

  • Publisher name

    Melandrium

  • Place of publication

    Slaný

  • Event location

    Prague

  • Event date

    Sep 5, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article