Ana Oliveira-Brochado () (EDGE, CESUR, DECIVIL-IST, Universidade Técnica de Lisboa) Francisco Vitorino Martins () (EDGE, Faculdade de Economia da Universidade do Porto)
Abstract
The aim of this work is to determine how well criteria designed to help the selection of the adequate number of mixture components perform in mixture regressions of normal data. We address this research question based on results of an extensive experimental design. The simulation experiment compares several criteria (26), including information criteria and classification-based criteria. In this full factorial design we manipulate 9 factors and 22 levels, namely: true number of segments (2 or 3), mean separation between segments (low, medium or high), number of consumers (100 or 300), number of observations per consumer (5 or 10), number of predictors (2, 6 or 10), measurement level of predictors (binary, metric or mixed), error variance (20% or 60%), minimum segment size (5-10%, 10-20% or 20-30%) and error distribution (normal versus uniform). The performance of the segment retention criteria is evaluated by their success rates; we also investigate the influence of experimental factors and their levels on success rates. The best results were obtained for the criteria AIC3, AIC4, HQ, ICLBIC and ICOMPLBIC. BIC and CAIC also perform well with large samples and a large number of market segments.
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Publisher Info
Paper provided by Universidade do Porto, Faculdade de Economia do Porto in its series FEP Working Papers with number
263.
Find related papers by JEL classification: C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation and Testing M31 - Business Administration and Business Economics; Marketing; Accounting - - Marketing and Advertising - - - Marketing
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