Binomial mixture estimation via Kiefer Wolfowitz MLE
Interior point solution of Kiefer-Wolfowitz NPMLE for mixture of binomials
Bmix(x, k, v = 300, collapse = TRUE, weights = NULL, ...)
x |
Count of "successes" for binomial observations |
k |
Number of trials for binomial observations |
v |
Grid Values for the mixing distribution defaults to equal spacing of length v on [eps, 1- eps], if v is scalar. |
collapse |
Collapse observations into cell counts. |
weights |
replicate weights for x obervations, should sum to 1 |
... |
Other arguments to be passed to KWDual to control optimization |
The predict method for Bmix
objects will compute means, medians or
modes of the posterior according to whether the Loss
argument is 2, 1
or 0, or posterior quantiles if Loss
is in (0,1).
An object of class density with components:
x |
grid midpoints of evaluation of the mixing density |
y |
function values of the mixing density at x |
g |
estimates of the mixture density at the distinct data values |
logLik |
Log Likelihood value at the estimate |
dy |
Bayes rule estimates of binomial probabilities for distinct data values |
status |
exit code from the optimizer |
R. Koenker
Kiefer, J. and J. Wolfowitz Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters Ann. Math. Statist. 27, (1956), 887-906.
Koenker, R and I. Mizera, (2013) “Convex Optimization, Shape Constraints, Compound Decisions, and Empirical Bayes Rules,” JASA, 109, 674–685.
Koenker, R. and J. Gu, (2017) REBayes: An R Package for Empirical Bayes Mixture Methods, Journal of Statistical Software, 82, 1–26.
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.