MVSIS {MFSIS}

Model-Free Feature Screening for Ultrahigh Dimensional Discriminant Analysis

Description

A marginal feature screening procedure based on empirical conditional distribution function. The response variable is categorical in discriminant analysis. This enables us to use the conditional distribution function to construct a new index for feature screening.

Usage

MVSIS(X, Y, nsis)

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 MVSIS. 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

Cui, H., Li, R., & Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional discriminant analysis. Journal of the American Statistical Association, 110(510), 630-641.

Examples

n=100;
p=200;
rho=0.5;
data=GendataLGM(n,p,rho)
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=MVSIS(X,Y,n/log(n));A