| WGLVmix {REBayes} | R Documentation |
Weighted NPMLE of Longitudinal Gaussian Mean and Variances Model
Description
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.
Usage
WGLVmix(y, id, w, u = 30, v = 30, ...)
Arguments
y |
A vector of observations |
id |
A strata indicator vector of the same length |
w |
A vector of weights |
u |
A vector of bin boundaries for the mean effects |
v |
A vector of bin boundaries for the variance effects |
... |
optional parameters to be passed to KWDual to control optimization |
Value
A list consisting of the following components:
u |
midpoints of mean bin boundaries |
v |
midpoints of variance bin boundaries |
fuv |
the function values of the mixing density. |
logLik |
log likelihood value for mean problem |
du |
Bayes rule estimate of the mixing density means. |
dv |
Bayes rule estimate of the mixing density variances. |
A |
Constraint matrix |
status |
Mosek convergence status |
Author(s)
R. Koenker and J. Gu
References
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.
See Also
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.