l2e_regression_MM: L2E multivariate regression - MM

In jocelynchi/L2E-package-demo: Robust Structured Regression via the L2 Criterion

L2E multivariate regression - MM

Description

l2e_regression_MM performs L2E multivariate regression via block coordinate descent with MM for updating beta and modified Newton for updating tau.

Usage

l2e_regression_MM(
  y,
  X,
  beta,
  tau,
  max_iter = 100,
  tol = 1e-04,
  Show.Time = TRUE
)

Arguments

y	response
X	Design matrix
beta	initial vector of regression coefficients
tau	initial precision estimate
max_iter	maximum number of iterations
tol	relative tolerance
Show.Time	Report the computing time

Value

Returns a list object containing the estimates for beta (vector) and tau (scalar), the number of outer block descent iterations until convergence (scalar), and the number of inner iterations per outer iteration for updating beta and eta (vectors)

Examples

# Bank data example
y <- bank$y
X <- as.matrix(bank[,1:13])
X0 <- as.matrix(cbind(rep(1,length(y)), X))
tau <- 1/mad(y)
b <- matrix(0, 14, 1)

sol <- l2e_regression_MM(y, X0, b, tau)
r <- y - X0 %*% sol$beta
ix <- which(abs(r) > 3/sol$tau)
l2e_fit <- X0 %*% sol$beta

plot(y, l2e_fit, ylab='Predicted values', pch=16, cex=0.8)
points(y[ix], l2e_fit[ix], pch=16, col='blue', cex=0.8)

jocelynchi/L2E-package-demo documentation built on Oct. 6, 2022, 6:05 a.m.