Assessing the Number of Components in Mixture Models: a Review
AbstractDespite the widespread application of finite mixture models, the decision of how many classes are required to adequately represent the data is, according to many authors, an important, but unsolved issue. This work aims to review, describe and organize the available approaches designed to help the selection of the adequate number of mixture components (including Monte Carlo test procedures, information criteria and classification-based criteria); we also provide some published simulation results about their relative performance, with the purpose of identifying the scenarios where each criterion is more effective (adequate).
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Bibliographic InfoPaper provided by Universidade do Porto, Faculdade de Economia do Porto in its series FEP Working Papers with number 194.
Length: 31 pages
Date of creation: Nov 2005
Date of revision:
Finite mixture; number of mixture components; information criteria; simulation studies.;
Find related papers by JEL classification:
- C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
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