Estimation of Linear Functionals in High-Dimensional Linear Models: From Sparsity to Nonsparsity [0.03%]
高维线性模型中线性泛函的估计：从稀疏到非稀疏
Junlong Zhao,Yang Zhou,Yufeng Liu
Junlong Zhao
High dimensional linear models are commonly used in practice. In many applications, one is interested in linear transformations β ⊤ x of regression coefficients β ∈ R p , where x is a specific point and is not requ...
Ye Tian,Yang Feng
Ye Tian
In this work, we study the transfer learning problem under highdimensional generalized linear models (GLMs), which aim to improve the fit on target data by borrowing information from useful source data. Given which sources to transfer, we p...
Statistical Inference for High-Dimensional Generalized Linear Models with Binary Outcomes [0.03%]
高维广义线性模型二值响应变量的统计推断方法研究
T Tony Cai,Zijian Guo,Rong Ma
T Tony Cai
This paper develops a unified statistical inference framework for high-dimensional binary generalized linear models (GLMs) with general link functions. Both unknown and known design distribution settings are considered. A two-step weighted ...
Statistical Inference for High-Dimensional Models via Recursive Online-Score Estimation [0.03%]
高维模型的统计推断及递归在线评分估计方法
Chengchun Shi,Rui Song,Wenbin Lu et al.
Chengchun Shi et al.
In this paper, we develop a new estimation and valid inference method for single or low-dimensional regression coefficients in high-dimensional generalized linear models. The number of the predictors is allowed to grow exponentially fast wi...
Zhao Chen,Jianqing Fan,Runze Li
Zhao Chen
Error variance estimation plays an important role in statistical inference for high dimensional regression models. This paper concerns with error variance estimation in high dimensional sparse additive model. We study the asymptotic behavio...