A Kiefer-Wolfowitz procedure for ML estimation of a Gaussian model with
dependent mean and variance components and weighted longitudinal data.
This version assumes a general bivariate distribution for the mixing
distribution. The defaults use a rather coarse bivariate gridding.
In contrast to the function GLVmix the full longitudinal data
structure is required for this function and the likelihood evaluation
reflects this difference.
WGLVmix(y, id, w, u = 30, v = 30, ...)A list consisting of the following components:
midpoints of mean bin boundaries
midpoints of variance bin boundaries
the function values of the mixing density.
log likelihood value for mean problem
Bayes rule estimate of the mixing density means.
Bayes rule estimate of the mixing density variances.
Constraint matrix
Mosek convergence status
A vector of observations
A strata indicator vector of the same length
A vector of weights
A vector of bin boundaries for the mean effects
A vector of bin boundaries for the variance effects
optional parameters to be passed to KWDual to control optimization
R. Koenker and J. Gu
Gu, J. and R. Koenker (2014) Heterogeneous Income Dynamics: An Empirical Bayes Perspective, JBES,35, 1-16.
Koenker, R. and J. Gu, (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1--26.
WTLVmix for an implementation assuming independent heterogeneity, GLVmix for an implementation that assumes the availability of only the summary statistics but not the full longitudinal data structure.