RobMixReg - Robust Mixture Regression
Finite mixture models are a popular technique for
modelling unobserved heterogeneity or to approximate general
distribution functions in a semi-parametric way. They are used
in a lot of different areas such as astronomy, biology,
economics, marketing or medicine. This package is the
implementation of popular robust mixture regression methods
based on different algorithms including: fleximix, finite
mixture models and latent class regression; CTLERob,
component-wise adaptive trimming likelihood estimation; mixbi,
bi-square estimation; mixL, Laplacian distribution; mixt,
t-distribution; TLE, trimmed likelihood estimation. The
implemented algorithms includes: CTLERob stands for
Component-wise adaptive Trimming Likelihood Estimation based
mixture regression; mixbi stands for mixture regression based
on bi-square estimation; mixLstands for mixture regression
based on Laplacian distribution; TLE stands for Trimmed
Likelihood Estimation based mixture regression. For more detail
of the algorithms, please refer to below references. Reference:
Chun Yu, Weixin Yao, Kun Chen (2017) <doi:10.1002/cjs.11310>.
NeyKov N, Filzmoser P, Dimova R et al. (2007)
<doi:10.1016/j.csda.2006.12.024>. Bai X, Yao W. Boyer JE (2012)
<doi:10.1016/j.csda.2012.01.016>. Wennan Chang, Xinyu Zhou,
Yong Zang, Chi Zhang, Sha Cao (2020) <arXiv:2005.11599>.