In this paper, we propose a robust variable selection to estimate and select relevant covariates for the finite mixture of linear regression models
by assuming that the error terms follow a Laplace distribution to the
data after trimming the high leverage points. We introduce a revised
Expectation-maximization (EM) algorithm for numerical computation.
Simulation studies indicate that the proposed method is robust to both
the high leverage points and outliers in the y-direction, and can obtain
a consistent variable selection in the case of outliers or heavy-tail error
distribution. Finally, we apply the proposed methodology to analyze a
real data.
Finite mixture of linear regression models Robustness EM-algorithm
Birincil Dil | İngilizce |
---|---|
Konular | İstatistik |
Bölüm | İstatistik |
Yazarlar | |
Yayımlanma Tarihi | 1 Nisan 2016 |
Yayımlandığı Sayı | Yıl 2016 Cilt: 45 Sayı: 2 |