Adaptive Lasso estimators for ultrahigh dimensional generalized linear models

作者：Wang Mingqiu; Wang Xiuli^*

来源：Statistics & Probability Letters, 2014, 89: 41-50.

DOI：10.1016/j.sp1.2014.02.015

• Adaptive Lasso
• Generalized linear models
• High dimensionality
• Variable selection

Generalized linear models have been more widely used than linear models which exclude categorical variables. The penalized method becomes an effective tool to study ultrahigh dimensional generalized linear models. In this paper, we study theoretical results of the adaptive Lasso for generalized linear models in terms of diverging number of parameters and ultrahigh dimensionality. The asymptotic results are examined by several simulation studies.

• 出版日期2014-6
• 单位曲阜师范大学