Construct the empirical log likelihood or empirical exponential family log likelihood for a mean.
EEF.profile(y, tmin = min(y) + 0.1, tmax = max(y) - 0.1, n.t = 25,
u = function(y, t) y - t)
EL.profile(y, tmin = min(y) + 0.1, tmax = max(y) - 0.1, n.t = 25,
u = function(y, t) y - t)A matrix with n.t rows. The first column contains the
values of the parameter used. The second column of the output
of EL.profile contains the values of the empirical
log likelihood. The second and third columns of the output of
EEF.profile contain two versions of the empirical
exponential family log-likelihood. The final column of the
output matrix contains the values of the Lagrange multiplier
used in the optimization procedure.
A vector or matrix of data
The minimum value of the range over which the
likelihood should be computed. This must be larger than
min(y).
The maximum value of the range over which the
likelihood should be computed. This must be smaller than
max(y).
The number of points between tmin and
tmax at which the value of the log-likelihood should be
computed.
A function of the data and the parameter.
Angelo J. Canty
These functions calculate the log likelihood for a mean using either
an empirical likelihood or an empirical exponential family likelihood.
They are supplied as part of the package boot for demonstration
purposes with the practicals in chapter 10 of Davison and Hinkley (1997).
The functions are not intended for general use and are not supported
as part of the bootpackage. For more general and more robust
code to calculate empirical likelihoods see Professor A. B. Owen's
empirical likelihood home page at the URL
https://artowen.su.domains/empirical/
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.