Robust estimation of the number of components for mixtures of linear regression
Date
2014-06-17
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Kansas State University
Abstract
In this report, we investigate a robust estimation of the number of components in the mixture of regression models using trimmed information criterion. Compared to the traditional information criterion, the trimmed criterion is robust and not sensitive to outliers. The superiority of the trimmed methods in comparison with the traditional information criterion methods is illustrated through a simulation study. A real data application is also used to illustrate the effectiveness of the trimmed model selection methods.
Description
Keywords
Mixture of linear regression models, Model selection, Robustness, Trimmed likelihood estimator
Graduation Month
August
Degree
Master of Science
Department
Department of Statistics
Major Professor
Weixin Yao
Date
2014
Type
Report