SLIC: SLIC function based on LIC with skewed error distributions

In SLIC: LIC for Distributed Skewed Regression

SLIC function based on LIC with skewed error distributions

Description

The SLIC function extends the LIC method by assuming that the error term follows a skewed distribution (Skew-Normal, Skew-t, or Skew-Laplace), thereby improving the length and information optimisation criterion.

Usage

SLIC(X, Y, alpha = 0.05, K = 10, nk = NULL, dist_type = "skew_normal")

Arguments

X	is a design matrix
Y	is a random response vector of observed values
alpha	is the significance level
K	is the number of subsets
nk	is the sample size of subsets
dist_type	is the type of skewed error distribution: "skew_normal", "skew_t", or "skew_laplace"

Value

MUopt, Bopt, MAEMUopt, MSEMUopt, opt, Yopt

Examples

set.seed(123)
n <- 1000
p <- 5
X <- matrix(rnorm(n * p), ncol = p)
beta <- runif(p, 1, 2)
e <- sn::rsn(n = n, xi = 0, omega = 1, alpha = 5)
Y <- X %*% beta + e
SLIC(X, Y, alpha = 0.05, K = 10, dist_type = "skew_normal")

SLIC documentation built on Aug. 12, 2025, 1:09 a.m.