Mixture Analysis with Noisy Data

Summary

Noisy data such as outliers cause difficulty in any model selection procedure. We present two general procedures. The first extends any model selection technique such as maximum likelihood or method of moments estimation to a procedure for obtaining mixture models. The second procedure makes a model selection technique more robust by weighting with members of the model family. Finally, we show how to combine these two procedures to obtain a robust, mixture modeling method