CLIC (version 0.1)

LICnew: Calculate the LIC estimator based on A-optimal and D-optimal criterion

Description

Calculate the LIC estimator based on A-optimal and D-optimal criterion

Usage

LICnew(X, Y, alpha, K, nk)

Value

A list containing:

E5

The LIC estimator based on A-optimal and D-optimal criterion.

Arguments

X

A matrix of observations (design matrix) with size n x p

Y

A vector of responses with length n

alpha

The significance level for confidence intervals

K

The number of subsets to consider

nk

The size of each subset

References

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

Guo, G., Sun, Y., Qian, G., & Wang, Q. (2022). LIC criterion for optimal subset selection in distributed interval estimation. Journal of Applied Statistics, 50(9), 1900-1920. tools:::Rd_expr_doi("10.1080/02664763.2022.2053949").

Chang, D., Guo, G. (2024). LIC: An R package for optimal subset selection for distributed data. SoftwareX, 28, 101909.

Jing, G., & Guo, G. (2025). TLIC: An R package for the LIC for T distribution regression analysis. SoftwareX, 30, 102132.

Chang, D., & Guo, G. (2025). Research on Distributed Redundant Data Estimation Based on LIC. IAENG International Journal of Applied Mathematics, 55(1), 1-6.

Gao, H., & Guo, G. (2025). LIC for Distributed Skewed Regression. IAENG International Journal of Applied Mathematics, 55(9), 2925-2930.

Zhang, C., & Guo, G. (2025). The optimal subset estimation of distributed redundant data. IAENG International Journal of Applied Mathematics, 55(2), 270–277.

Jing, G., & Guo, G. (2025). Student LIC for distributed estimation. IAENG International Journal of Applied Mathematics, 55(3), 575–581.

Liu, Q., & Guo, G. (2025). Distributed estimation of redundant data. IAENG International Journal of Applied Mathematics, 55(2), 332–337.

Examples

p = 6; n = 1000; K = 2; nk = 200; 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;
LICnew(X = X, Y = Y, alpha = alpha, K = K, nk = nk)