update_beta_MM_sparse {L2E}

Beta update in L2E sparse regression - MM

Description

update_beta_MM_sparse updates beta for L2E sparse regression using the distance penalty

Usage

update_beta_MM_sparse(
  y,
  X,
  beta,
  tau,
  k,
  rho,
  stepsize = 0.9,
  sigma = 0.5,
  max_iter = 100,
  tol = 1e-04
)

Arguments

y

Response vector

X

Design matrix

beta

Initial vector of regression coefficients

tau

Initial precision estimate

k

The number of nonzero entries in the estimated coefficients

rho

The parameter in the proximal distance algorithm

stepsize

The stepsize parameter for the MM algorithm (0, 1)

sigma

The halving parameter sigma (0, 1)

max_iter

Maximum number of iterations

tol

Relative tolerance

Value

Returns a list object containing the new estimate for beta (vector) and the number of iterations (scalar) the update step utilized