Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6870318 | Computational Statistics & Data Analysis | 2014 | 13 Pages |
Abstract
Recently, finite mixture models have been used to model the distribution of the error terms in multivariate linear regression analysis. In particular, Gaussian mixture models have been employed. A novel approach that assumes that the error terms follow a finite mixture of t distributions is introduced. This assumption allows for an extension of multivariate linear regression models, making these models more versatile and robust against the presence of outliers in the error term distribution. The issues of model identifiability and maximum likelihood estimation are addressed. In particular, identifiability conditions are provided and an Expectation-Maximisation algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments and compared to the estimators from the Gaussian mixture models. Results from the analysis of two real datasets are presented.
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Giuliano Galimberti, Gabriele Soffritti,