کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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1149967 | 957907 | 2011 | 8 صفحه PDF | دانلود رایگان |
A novel family of mixture models is introduced based on modified t-factor analyzers. Modified factor analyzers were recently introduced within the Gaussian context and our work presents a more flexible and robust alternative. We introduce a family of mixtures of modified t-factor analyzers that uses this generalized version of the factor analysis covariance structure. We apply this family within three paradigms: model-based clustering; model-based classification; and model-based discriminant analysis. In addition, we apply the recently published Gaussian analogue to this family under the model-based classification and discriminant analysis paradigms for the first time. Parameter estimation is carried out within the alternating expectation-conditional maximization framework and the Bayesian information criterion is used for model selection. Two real data sets are used to compare our approach to other popular model-based approaches; in these comparisons, the chosen mixtures of modified t-factor analyzers model performs favourably. We conclude with a summary and suggestions for future work.
Journal: Journal of Statistical Planning and Inference - Volume 141, Issue 4, April 2011, Pages 1479–1486