Order Selection in Finite Mixture Models
نویسندگان
چکیده
Jiahua Chen, Abbas Khalili Department of Statistics and Actuarial Science University of Waterloo Abstract A fundamental and challenging problem in the application of finite mixture models is to make inference on the order of the model. In this paper, we develop a new penalized likelihood approach to the order selection problem. The new method deviates from the information-based methods such as AIC and BIC by introducing two penalty functions which depend on the mixing proportions and the component parameters. The new method is shown to be consistent and have other good properties. Simulations show that the method has much better performance compared to a number of existing methods. We further demonstrate the new method by analyzing two well known real data sets. Short Title: ORDER SELECTION
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