Skip navigation
User training | Reference and search service

Library catalog

EDS
b-on
More
resources
Content aggregators
Please use this identifier to cite or link to this item:

acessibilidade

http://hdl.handle.net/10071/20569
acessibilidade
Title: Determining the number of components in mixture regression models: an experimental design
Authors: Brochado, A.
Martins, F. V.
Keywords: Information criterion
Classification criterion
Component
Experimental design
Simulation
Issue Date: 2020
Publisher: Economics Bulletin
Abstract: Despite the popularity of mixture regression models, the decision of how many components to retain remains an open issue. This study thus sought to compare the performance of 26 information and classification criteria. Each criterion was evaluated in terms of that component's success rate. The research's full experimental design included manipulating 9 factors and 22 levels. The best results were obtained for 5 criteria: Akaike information criteria 3 (AIC3), AIC4, Hannan-Quinn information criteria, integrated completed likelihood (ICL) Bayesian information criteria (BIC) and ICL with BIC approximation. Each criterion's performance varied according to the experimental conditions.
Peer reviewed: yes
URI: http://hdl.handle.net/10071/20569
ISSN: 1545-2921
Ciência-IUL: https://ciencia.iscte-iul.pt/id/ci-pub-73037
Accession number: WOS:000538980600006
Appears in Collections:DINÂMIA'CET-RI - Artigo em revista científica internacional com arbitragem científica

Files in This Item:
acessibilidade
File Description SizeFormat 
EB-20-V40-I2-P126.pdfVersão Editora479.01 kBAdobe PDFView/Open


FacebookTwitterDeliciousLinkedInDiggGoogle BookmarksMySpace
Formato BibTex MendeleyEndnote Currículo DeGóis 

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.