Beta update in L2E sparse regression - MM

update_beta_MM_sparse updates beta for L2E sparse regression using the distance penalty

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