Self-concordant empirical likelihood for a vector mean
emplik(
dat,
mu = rep(0, ncol(dat)),
lam = rep(0, ncol(dat)),
eps = 1/nrow(dat),
M = 1e+30,
thresh = 1e-30,
itermax = 100
)
a list with components
logelr
log empirical likelihood ratio.
lam
Lagrange multiplier (vector of length d
).
wts
n
vector of observation weights (probabilities).
conv
boolean indicating convergence.
niter
number of iteration until convergence.
ndec
Newton decrement.
gradnorm
norm of gradient of log empirical likelihood.
n
by d
matrix of d
-variate observations
d
vector of hypothesized mean of dat
starting values for Lagrange multiplier vector, default to zero vector
lower cutoff for \(-\log\), with default 1/nrow(dat)
upper cutoff for \(-\log\).
convergence threshold for log likelihood (default of 1e-30
is aggressive)
upper bound on number of Newton steps.
Art Owen, C++
port by Leo Belzile
Owen, A.B. (2013). Self-concordance for empirical likelihood, Canadian Journal of Statistics, 41(3), 387--397.