beta_LW: Caculate the estimators of beta on the LEV-opt#'

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

Caculate the estimators of beta on the LEV-opt#'

Description

Caculate the estimators of beta on the LEV-opt#'

Usage

beta_LW(X, Y, K, nk)

Arguments

X	is the observation matrix
Y	is the response vector
K	is the number of subsets
nk	is the length of subsets

Value

A list containing:

betalev	The estimator of beta on the LEV-opt subset.
betam	The mean of the beta estimators across all K subsets.
AMSE	The Average Mean Squared Error (AMSE) for the estimator.
WMSE	The Weighted Mean Squared Error (WMSE) for the estimator.
MSElevb	The Mean Squared Error (MSE) of the LEV-opt estimator compared to the true beta.
MSEb	The Mean Squared Error (MSE) of the mean estimator (betam) compared to the true beta.
MSEyleva	The Mean Squared Error (MSE) of the LEV-opt estimator on the subset with the maximum hat value (Xleva).
MSEyleviy	The Mean Squared Error (MSE) of the LEV-opt estimator on the subset with the minimum hat value (Xlevi).
MSEW	The Mean Squared Error (MSE) of the weighted estimator (Wbeta) compared to the true beta.
MSEw	The Mean Squared Error (MSE) of the weighted estimator (wbeta) compared to the true beta.

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")}

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