GLVmix {REBayes} | R Documentation |
NPMLE of Gaussian Location-Scale Mixture Model
Description
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.
Usage
GLVmix(t, s, m, u = 30, v = 30, ...)
Arguments
t |
A vector of location estimates |
s |
A vector of variance estimates |
m |
A vector of sample sizes of the same length as t and s, or if scalar a common sample size length |
u |
A vector of bin boundaries for the location 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, and WGLVmix for a version that requires access to a full longitudinal data structure.