Jingyuan Liu,Lejia Lou,Runze Li
Jingyuan Liu
The partially linear model (PLM) is a useful semiparametric extension of the linear model that has been well studied in the statistical literature. This paper proposes a variable selection procedure for the PLM with ultrahigh dimensional pr...
Irina Gaynanova,Tianying Wang
Irina Gaynanova
We consider the problem of high-dimensional classification between two groups with unequal covariance matrices. Rather than estimating the full quadratic discriminant rule, we propose to perform simultaneous variable selection and linear di...
Hyokyoung G Hong,Qi Zheng,Yi Li
Hyokyoung G Hong
Forward regression, a classical variable screening method, has been widely used for model building when the number of covariates is relatively low. However, forward regression is seldom used in high-dimensional settings because of the cumbe...
Robust network-based analysis of the associations between (epi)genetic measurements [0.03%]
基于网络的遗传和表观遗传来测量值之间关联性的稳健分析方法研究
Cen Wu,Qingzhao Zhang,Yu Jiang et al.
Cen Wu et al.
With its important biological implications, modeling the associations of gene expression (GE) and copy number variation (CNV) has been extensively conducted. Such analysis is challenging because of the high data dimensionality, lack of know...
Linlin Dai,Kani Chen,Zhihua Sun et al.
Linlin Dai et al.
This paper studies the asymptotic properties of a sparse linear regression estimator, referred to as broken adaptive ridge (BAR) estimator, resulting from an L 0-based iteratively reweighted L 2 penalization algorithm using the ridge estima...
Joint sufficient dimension reduction for estimating continuous treatment effect functions [0.03%]
连续治疗效应函数的联合充分降维法
Ming-Yueh Huang,Kwun Chuen Gary Chan
Ming-Yueh Huang
The estimation of continuous treatment effect functions using observational data often requires parametric specification of the effect curves, the conditional distributions of outcomes and treatment assignments given multi-dimensional covar...
Semiparametric regression for measurement error model with heteroscedastic error [0.03%]
异方差测量误差模型的半参数回归分析
Mengyan Li,Yanyuan Ma,Runze Li
Mengyan Li
Covariate measurement error is a common problem. Improper treatment of measurement errors may affect the quality of estimation and the accuracy of inference. Extensive literature exists on homoscedastic measurement error models, but little ...
David Hong,Laura Balzano,Jeffrey A Fessler
David Hong
Principal Component Analysis (PCA) is a classical method for reducing the dimensionality of data by projecting them onto a subspace that captures most of their variation. Effective use of PCA in modern applications requires understanding it...
Efficient test-based variable selection for high-dimensional linear models [0.03%]
高维线性模型的高效检验基变量选择方法
Siliang Gong,Kai Zhang,Yufeng Liu
Siliang Gong
Variable selection plays a fundamental role in high-dimensional data analysis. Various methods have been developed for variable selection in recent years. Well-known examples are forward stepwise regression (FSR) and least angle regression ...
Sheng Fu,Sanguo Zhang,Yufeng Liu
Sheng Fu
Large-margin classifiers are powerful techniques for classification problems. Although binary large-margin classifiers are heavily studied, multicategory problems are more complicated and challenging. A common approach is to construct k dif...