A Robustified Metalearning Procedure for Regression Estimators

0510554 - ÚI 2020 RIV CZ eng C - Konferenční příspěvek (zahraniční konf.)
Kalina, Jan - Neoral, A.
A Robustified Metalearning Procedure for Regression Estimators.
The 13th International Days of Statistics and Economics Conference Proceedings. Slaný: Melandrium, 2019 - (Löster, T.; Pavelka, T.), s. 617-626. ISBN 978-80-87990-18-6.
[International Days of Statistics and Economics /13./. Prague (CZ), 05.09.2019-07.09.2019]
Grant ostatní: GA ČR(CZ) GA17-07384S
Institucionální podpora: RVO:67985807
Klíčová slova: model choice * computational statistics * robustness * variable selection
Obor OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
https://msed.vse.cz/msed_2019/sbornik/toc.html

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.
Trvalý link: http://hdl.handle.net/11104/0300999