How to down-weight observations in robust regression: A metalearning study

0493805 - ÚI 2019 RIV CZ eng C - Conference Paper (international conference)
Kalina, Jan - Pitra, Zbyněk
How to down-weight observations in robust regression: A metalearning study.
Mathematical Methods in Economics 2018. Conference Proceedings. Prague: MatfyzPress, 2018 - (Váchová, L.; Kratochvíl, V.), s. 204-209. ISBN 978-80-7378-371-6.
[MME 2018. International Conference Mathematical Methods in Economics /36./. Jindřichův Hradec (CZ), 12.09.2018-14.09.2018]
R&D Projects: GA ČR GA17-07384S; GA ČR GA17-01251S
Institutional support: RVO:67985807
Keywords : metalearning * robust statistics * linear regression * outliers
OECD category: Statistics and probability

Metalearning is becoming an increasingly important methodology for extracting knowledge from a data base of available training data sets to a new (independent) data set. The concept of metalearning is becoming popular in statistical learning and there is an increasing number of metalearning applications also in the analysis of economic data sets. Still, not much attention has been paid to its limitations and disadvantages. For this purpose, we use various linear regression estimators (including highly robust ones) over a set of 30 data sets with economic background and perform a metalearning study over them as well as over the same data sets after an artificial contamination. We focus on comparing the prediction performance of the least weighted squares estimator with various weighting schemes. A broader spectrum of classification methods is applied and a support vector machine turns out to yield the best results. While results of a leave-1-out cross validation are very different from results of autovalidation, we realize that metalearning is highly unstable and its results should be interpreted with care. We also focus on discussing all possible limitations of the metalearning methodology in general.
Permanent Link: http://hdl.handle.net/11104/0287108