Inference in generalized linear models with robustness to misspecified variances [0.03%]
具有稳健性误指定方差的广义线性模型中的推理
Riccardo De Santis,Jelle J Goeman,Samuel Joseph Davenport et al.
Riccardo De Santis et al.
Generalized linear models usually assume a common dispersion parameter, an assumption that is seldom true in practice. Consequently, standard parametric methods may suffer appreciable loss of type I error control. As an alternative, we pres...
Simultaneous inference for generalized linear models with unmeasured confounders [0.03%]
存在未测量混杂因素的广义线性模型的同步推断
Jin-Hong Du,Larry Wasserman,Kathryn Roeder
Jin-Hong Du
Tens of thousands of simultaneous hypothesis tests are routinely performed in genomic studies to identify differentially expressed genes. However, due to unmeasured confounders, many standard statistical approaches may be substantially bias...
Estimation and Inference for High-Dimensional Generalized Linear Models with Knowledge Transfer [0.03%]
基于知识迁移的高维广义线性模型的估计和推断
Sai Li,Linjun Zhang,T Tony Cai et al.
Sai Li et al.
Transfer learning provides a powerful tool for incorporating data from related studies into a target study of interest. In epidemiology and medical studies, the classification of a target disease could borrow information across other relate...
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 ...
Variable Selection with Prior Information for Generalized Linear Models via the Prior LASSO Method [0.03%]
基于先验信息的广义线性模型变量选择的Prior LASSO方法
Yuan Jiang,Yunxiao He,Heping Zhang
Yuan Jiang
LASSO is a popular statistical tool often used in conjunction with generalized linear models that can simultaneously select variables and estimate parameters. When there are many variables of interest, as in current biological and biomedica...
Semiparametric Analysis of Heterogeneous Data Using Varying-Scale Generalized Linear Models [0.03%]
利用变化尺度广义线性模型进行异质数据的半参数分析
Minge Xie,Douglas G Simpson,Raymond J Carroll
Minge Xie
This article describes a class of heteroscedastic generalized linear regression models in which a subset of the regression parameters are rescaled nonparametrically, and develops efficient semiparametric inferences for the parametric compon...