Order Selection in Finite Mixture Models With a Nonsmooth Penalty

Order Selection in Finite Mixture Models

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 ...

An Overview of the New Feature Selection Methods in Finite Mixture of Regression Models

Variable (feature) selection has attracted much attention in contemporary statistical learning and recent scientific research. This is mainly due to the rapid advancement in modern technology that allows scientists to collect data of unprecedented size and complexity. One type of statistical problem in such applications is concerned with modeling an output variable as a function of a sma...

Variable Selection in Finite Mixture of Regression Models

In the applications of finite mixture of regression (FMR) models, often many covariates are used, and their contributions to the response variable vary from one component to another of the mixture model. This creates a complex variable selection problem. Existing methods, such as the Akaike information criterion and the Bayes information criterion, are computationally expensive as the number of...

On Model Selection and Concavity for Finite Mixture Models

Unsupervised Selection and Estimation of Finite Mixture Models

We propose a new method for fitting mixture models that performs component selection and does not require external initialization. The novelty of our approach includes: a minimum message length (MML) type model selection criterion; the inclusion of the criterion into the expectation-maximization (EM) algorithm (which also increases its ability to escape from local maxima); an initialization str...