A Kiefer-Wolfowitz procedure for ML estimation of a Gaussian model with
possibly dependent mean and variance components. This version differs from
WGLVmix in that it doesn't assume the data is in longitudinal form.
This version assumes a general bivariate distribution for the mixing
distribution. The defaults use a rather coarse bivariate gridding.
GLVmix(t, s, m, 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 location estimates
A vector of variance estimates
A vector of sample sizes of the same length as t and s, or if scalar a common sample size length
A vector of bin boundaries for the location 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, and WGLVmix for a version that requires access to a full longitudinal data structure.