A constrained robust proposal for mixture modeling avoiding spurious solutions

作者：Garcia Escudero L A; Gordaliza A^*; Mayo Iscar A

来源：Advances in Data Analysis and Classification, 2014, 8(1): 27-43.

DOI：10.1007/s11634-013-0153-3

• Mixture models
• Constraints
• Robustness
• Trimming
• Eigenvalues restrictions
• Maximum likelihood estimation

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.

• 出版日期2014-3