Penalized multinomial mixture logit model
Copyright policy )Penalized multinomial mixture logit model
A classification problem is considered where the observed classes are mixtures of some subclasses. The multinomial logit model is used to model the dependence between subclasses labels and predictors. A version of the EM algorithm is proposed for fitting the resulting mixture model. This algorithm maximizes a penalized log-likelihood, helping to avoid the overfitting problem. Results of simulations and applications to medical data are presented.
- University of Guelph Canada
- McMaster University Canada
Statistics and Probability, Computational Mathematics, Generalized linear models (logistic models), Classification and discrimination; cluster analysis (statistical aspects), penalized maximum likelihood estimation, classification, Statistics, Probability and Uncertainty, Computational methods for problems pertaining to statistics, EM algorithm
Statistics and Probability, Computational Mathematics, Generalized linear models (logistic models), Classification and discrimination; cluster analysis (statistical aspects), penalized maximum likelihood estimation, classification, Statistics, Probability and Uncertainty, Computational methods for problems pertaining to statistics, EM algorithm
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