J
Jianqing Fan
Researcher at Princeton University
Publications - 533
Citations - 65172
Jianqing Fan is an academic researcher from Princeton University. The author has contributed to research in topics: Estimator & Covariance. The author has an hindex of 104, co-authored 488 publications receiving 58039 citations. Previous affiliations of Jianqing Fan include Clemson University & University of California.
Papers
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Journal ArticleDOI
Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
Jianqing Fan,Runze Li +1 more
TL;DR: In this article, penalized likelihood approaches are proposed to handle variable selection problems, and it is shown that the newly proposed estimators perform as well as the oracle procedure in variable selection; namely, they work as well if the correct submodel were known.
Book
Local polynomial modelling and its applications
Jianqing Fan,Irène Gijbels +1 more
TL;DR: Applications of Local Polynomial Modeling in Nonlinear Time Series and Automatic Determination of Model Complexity and Framework for Local polynomial regression.
Journal ArticleDOI
Sure independence screening for ultrahigh dimensional feature space
Jianqing Fan,Jinchi Lv +1 more
TL;DR: In this article, the authors introduce the concept of sure screening and propose a sure screening method that is based on correlation learning, called sure independence screening, to reduce dimensionality from high to a moderate scale that is below the sample size.
Posted Content
Sure Independence Screening for Ultra-High Dimensional Feature Space
Jianqing Fan,Jinchi Lv +1 more
TL;DR: The concept of sure screening is introduced and a sure screening method that is based on correlation learning, called sure independence screening, is proposed to reduce dimensionality from high to a moderate scale that is below the sample size.
Journal ArticleDOI
Design-adaptive Nonparametric Regression
TL;DR: In this paper, a weighted local linear regression (LR) was proposed for nonparametric regression, which has high asymptotic efficiency and adapts to both random and fixed designs, to both highly clustered and nearly uniform designs, and even to both interior and boundary points.