Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 71, Issue -, Pages 128-137Publisher
ELSEVIER
DOI: 10.1016/j.csda.2013.06.022
Keywords
Least absolute deviation; EM algorithm; Mixture regression model; Normal mixture; Laplace distribution
Funding
- NSF [DMS1205276]
- Division Of Mathematical Sciences
- Direct For Mathematical & Physical Scien [1205276] Funding Source: National Science Foundation
Ask authors/readers for more resources
A robust estimation procedure for mixture linear regression models is proposed by assuming that the error terms follow a Laplace distribution. Using the fact that the Laplace distribution can be written as a scale mixture of a normal and a latent distribution, this procedure is implemented by an EM algorithm which incorporates two types of missing information from the mixture class membership and the latent variable. Finite sample performance of the proposed algorithm is evaluated by simulations. The proposed method is compared with other procedures, and a sensitivity study is also conducted based on a real data set. Published by Elsevier B.V.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available