A constrained robust proposal for mixture modeling avoiding spurious solutions

L. García-Escudero, A. Gordaliza () and A. Mayo-Iscar

Advances in Data Analysis and Classification, 2014, vol. 8, issue 1, 27-43

Abstract: The high prevalence of spurious solutions and the disturbing effect of outlying observations in mixture modeling are well known problems that pose serious difficulties for non-expert practitioners of this kind of models in different applied areas. An approach which combines the use of Trimmed Maximum Likelihood ideas and the imposition of restrictions on the maximization problem will be presented and studied in this paper. The proposed methodology is shown to have nice mathematical properties as well as good performance in avoiding the appearance of spurious solutions in a quite automatic manner. Copyright Springer-Verlag Berlin Heidelberg 2014

Keywords: Mixture models; Constraints; Robustness; Trimming; Eigenvalues restrictions; Maximum likelihood estimation; 62H30 (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (7)

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DOI: 10.1007/s11634-013-0153-3

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