  In this article, we introduce a new variable selection technique through
trimming for finite mixture of regression models. Compared to the traditional
variable selection techniques, the new method is robust and not sensitive to
outliers. The estimation algorithm is introduced and numerical studies are
conducted to examine the finite sample performance of the proposed procedure
and to compare it with other existing methods.
