EEF.profile {boot}  R Documentation 
Empirical Likelihoods
Description
Construct the empirical log likelihood or empirical exponential family log likelihood for a mean.
Usage
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)
Arguments
y 
A vector or matrix of data 
tmin 
The minimum value of the range over which the
likelihood should be computed. This must be larger than

tmax 
The maximum value of the range over which the
likelihood should be computed. This must be smaller than

n.t 
The number of points between 
u 
A function of the data and the parameter. 
Details
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 boot
package. 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/
Value
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 loglikelihood. The final column of the
output matrix contains the values of the Lagrange multiplier
used in the optimization procedure.
Author(s)
Angelo J. Canty
References
Davison, A. C. and Hinkley, D. V. (1997) Bootstrap Methods and Their Application. Cambridge University Press.