minimum density power divergence
1.0.0
This package estimates coefficients of a high-dimensional linear regression model. Significantly different from the existing studies, we adopt loss functions based on minimum density power divergence (MDPD) criteria. Multiple published studies have shown that this approach outperforms alternatives under low dimensional situations, especially when normality assumption is violated. We extend this method to a high dimensional situation and also observe the robust performance. Penalization is used for identification and regularized estimation. Computationally, we develop an effective algorithm which utilizes the coordinate descent. Simulation shows that the proposed approach has satisfactory performance.
Currently Released Under GPLv3
Yangguang Zang yangguang.zang@gmail.com; Qingzhao Zhang qzzhang.wise@gmail.com; Shuangge Ma shuangge.ma@yale.edu
Yangguang Zang