Kfilter: The Kolmogorov filter for variable screening

In MFSIS: Model-Free Sure Independent Screening Procedures

The Kolmogorov filter for variable screening

Description

A new model-free screening method called the fused Kolmogorov filter is proposed for high-dimensional data analysis. This new method is fully nonparametric and can work with many types of covariates and response variables, including continuous, discrete and categorical variables.

Usage

Kfilter(X, Y, nsis = (dim(X)[1])/log(dim(X)[1]))

Arguments

X	The design matrix of dimensions n * p. Each row is an observation vector.
Y	The response vector of dimension n * 1.
nsis	Number of predictors recruited by SIS. The default is n/log(n).

Value

the labels of first nsis largest active set of all predictors

Author(s)

Xuewei Cheng [e-mail]

References

Mai, Q., & Zou, H. (2013). The Kolmogorov filter for variable screening in high-dimensional binary classification. Biometrika, 100(1), 229-234.

Mai, Q., & Zou, H. (2015). The fused Kolmogorov filter: A nonparametric model-free screening method. The Annals of Statistics, 43(4), 1471-1497.

Examples

n=100;
p=200;
rho=0.5;
data=GendataLM(n,p,rho,error="gaussian")
data=cbind(data[[1]],data[[2]])
colnames(data)[1:ncol(data)]=c(paste0("X",1:(ncol(data)-1)),"Y")
data=as.matrix(data)
X=data[,1:(ncol(data)-1)];
Y=data[,ncol(data)];
A=Kfilter(X,Y,n/log(n));A

MFSIS documentation built on June 22, 2024, 9:42 a.m.