huber.reg.adaptive.skew {SFM} | R Documentation |
Adaptive Huber Regression for Skew Factor Models
Description
Performs adaptive Huber regression tailored for skew factor models, and returns the estimated regression coefficients in a matrix (loading matrix) format.
Usage
huber.reg.adaptive.skew(
X,
Y,
tau = 1.35,
max_iterations = 100,
tolerance = 1e-06,
n_factors = 1
)
Arguments
X |
A matrix of predictor variables. |
Y |
A vector of response variables. |
tau |
Initial robustification parameter (default is 1.35). |
max_iterations |
Maximum number of iterations (default is 100). |
tolerance |
Convergence tolerance (default is 1e-6). |
n_factors |
The number of factors (columns) for the loading matrix (default is 1). |
Value
A matrix of estimated regression coefficients with dimensions 'p x n_factors'.
Examples
# Generate some example data for skew factor models
set.seed(123)
n <- 200
d <- 10
beta <- rep(1, d)
skew_factor <- rnorm(n) # Adding a skew factor
X <- matrix(rnorm(n * d), n, d)
err <- rnorm(n)
Y <- 1 + skew_factor + X %*% beta + err
# Perform adaptive Huber regression for skew factor model
loading_matrix <- huber.reg.adaptive.skew(X, Y, n_factors = 3)
print(loading_matrix)
[Package SFM version 0.1.0 Index]