A Comparison of Robust Model Choice Criteria Within a Metalearning Study

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F20%3A00531580" target="_blank" >RIV/67985807:_____/20:00531580 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-030-48814-7_7" target="_blank" >http://dx.doi.org/10.1007/978-3-030-48814-7_7</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-48814-7_7" target="_blank" >10.1007/978-3-030-48814-7_7</a>

Result language

angličtina

Original language name

A Comparison of Robust Model Choice Criteria Within a Metalearning Study

Original language description

The methodology of automatic method selection (metalearning) allows to recommend the most suitable method (e.g. algorithm or statistical estimator) from several alternatives for a given dataset, based on information learned over a training database of datasets. Practitioners have become accustomed to using metalearning in the context of regression modeling, which is useful in a variety of applications in different fields. Still, none of previous metalearning studies on regression targeted at regression complexity issues and the majority of available metalearning studies for regression considered the standard mean square error as the prediction error measure. In this paper, a metalearning study focused on comparing different method selection criteria for the regression task is presented. A prediction rule, recommending the best regression estimator (possibly robust), is constructed over 31 training datasets. These are publicly available datasets, in which the linear model was carefully examined to be suitable. The results with the highest classification accuracy are obtained if the choice of the best estimator is based on robust versions of Akaike information criterion, particularly the version derived from MM-estimators. The work also advocates an implicitly weighted robust prediction mean square error.

Czech name

—

Czech description

—

Type

D - Article in proceedings

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2020

Confidentiality

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

Article name in the collection

Analytical Methods in Statistics

ISBN

978-3-030-48813-0

ISSN

—

e-ISSN

—

Number of pages

17

Pages from-to

125-141

Publisher name

Springer

Place of publication

Cham

Event location

Liberec

Event date

Sep 16, 2019

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

—