MSEcom: Caculate the MSE values of the COR criterion in simulation

In COR: The COR for Optimal Subset Selection in Distributed Estimation

Caculate the MSE values of the COR criterion in simulation

Description

Caculate the MSE values of the COR criterion in simulation

Usage

MSEcom(K = K, nk = nk, alpha = alpha, X = X, y = y)

Arguments

K	is the number of subsets
nk	is the length of subsets
alpha	is the significance level
X	is the observation matrix
y	is the response vector

Value

A list containing:

MSEx	The Mean Squared Error between the true beta and the estimate betax based on the COR.
MSEA	The Mean Squared Error between the true beta and the estimate betaA based on the least squares estimate for subset A.
MSEc	The Mean Squared Error between the true beta and the estimate betac based on the COR-selected subset.
MSEm	The Mean Squared Error between the true beta and the median estimator betamm across all subsets.
MSEa	The Mean Squared Error between the true beta and the mean estimator betaa across all subsets.

References

Guo, G., Song, H. & Zhu, L. The COR criterion for optimal subset selection in distributed estimation. Statistics and Computing, 34, 163 (2024). \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s11222-024-10471-z")}

Examples

p=6;n=1000;K=2;nk=500;alpha=0.05;sigma=1
e=rnorm(n,0,sigma); beta=c(sort(c(runif(p,0,1))));
data=c(rnorm(n*p,5,10));X=matrix(data, ncol=p);
y=X%*%beta+e;
MSEcom(K=K,nk=nk,alpha=alpha,X=X,y=y)

COR documentation built on April 4, 2025, 5:13 a.m.