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Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
TLDR
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.
Local polynomial modelling and its applications
TLDR
Applications of Local Polynomial Modeling in Nonlinear Time Series and Automatic Determination of Model Complexity and Framework for Local polynomial regression.
Sure independence screening for ultrahigh dimensional feature space
TLDR
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.
Design-adaptive Nonparametric Regression
Abstract In this article we study the method of nonparametric regression based on a weighted local linear regression. This method has advantages over other popular kernel methods. Moreover, such a
Generalized Partially Linear Single-Index Models
Abstract The typical generalized linear model for a regression of a response Y on predictors (X, Z) has conditional mean function based on a linear combination of (X, Z). We generalize these models
Profile likelihood inferences on semiparametric varying-coefficient partially linear models
Varying-coefficient partially linear models are frequently used in statistical modelling, but their estimation and inference have not been systematically studied. This paper proposes a profile
Nonconcave penalized likelihood with a diverging number of parameters
A class of variable selection procedures for parametric models via nonconcave penalized likelihood was proposed by Fan and Li to simultaneously estimate parameters and select important variables.
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