Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …
Page 1 The Annals of Statistics 2013, Vol. 41, No. 1, 342–369 DOI: 10.1214/13-AOS1087 © …
Conditional quantile screening in ultrahigh-dimensional heterogeneous data
To accommodate the heterogeneity that is often present in ultrahigh-dimensional data, we
propose a conditional quantile screening method, which enables us to select features that …
propose a conditional quantile screening method, which enables us to select features that …
A selective overview of feature screening for ultrahigh-dimensional data
High-dimensional data have frequently been collected in many scientific areas including
genomewide association study, biomedical imaging, tomography, tumor classifications, and …
genomewide association study, biomedical imaging, tomography, tumor classifications, and …
Nonparametric independence screening in sparse ultra-high-dimensional varying coefficient models
J Fan, Y Ma, W Dai - Journal of the American Statistical Association, 2014 - Taylor & Francis
The varying coefficient model is an important class of nonparametric statistical model, which
allows us to examine how the effects of covariates vary with exposure variables. When the …
allows us to examine how the effects of covariates vary with exposure variables. When the …
Interaction screening for ultrahigh-dimensional data
In ultrahigh-dimensional data analysis, it is extremely challenging to identify important
interaction effects, and a top concern in practice is computational feasibility. For a dataset …
interaction effects, and a top concern in practice is computational feasibility. For a dataset …
Sure independence screening in generalized linear models with NP-dimensionality
Ultrahigh-dimensional variable selection plays an increasingly important role in
contemporary scientific discoveries and statistical research. Among others, Fan and Lv [JR …
contemporary scientific discoveries and statistical research. Among others, Fan and Lv [JR …
Variable screening via quantile partial correlation
In quantile linear regression with ultrahigh-dimensional data, we propose an algorithm for
screening all candidate variables and subsequently selecting relevant predictors …
screening all candidate variables and subsequently selecting relevant predictors …
Nonparametric independence screening in sparse ultra-high-dimensional additive models
A variable screening procedure via correlation learning was proposed by Fan and Lv (2008)
to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true …
to reduce dimensionality in sparse ultra-high-dimensional models. Even when the true …
[HTML][HTML] Globally adaptive quantile regression with ultra-high dimensional data
Quantile regression has become a valuable tool to analyze heterogeneous covaraite-
response associations that are often encountered in practice. The development of quantile …
response associations that are often encountered in practice. The development of quantile …
Linear hypothesis testing in dense high-dimensional linear models
We propose a methodology for testing linear hypothesis in high-dimensional linear models.
The proposed test does not impose any restriction on the size of the model, that is, model …
The proposed test does not impose any restriction on the size of the model, that is, model …