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]