A Likelihood Ratio Framework for High Dimensional Semiparametric Regression

Understand

We propose a likelihood ratio based inferential framework for high dimensional semiparametric generalized linear models.

• This framework addresses a variety of challenging problems in high dimensional data analysis, including incomplete data, selection bias, and heterogeneous multitask learning.
• Our work has three main contributions.
• (i) We develop a regularized statistical chromatography approach to infer the parameter of interest under the proposed semiparametric generalized linear model without the need of estimating the unknown base measure function.