We describe a Monte Carlo method to approximate the maximum likelihood estimate (MLE), when there are missing data and the observed data likelihood is not available in closed form. This method uses simulated missing data that are independent and identically distributed and independent of the observed data. Our Monte Carlo approximation to the MLE is a consistent and asymptotically normal estimate of the minimizer θ* of the Kullback–Leibler information, as both Monte Carlo and observed data sample sizes go to infinity simultaneously. Plug-in estimates of the asymptotic variance are provided for constructing confidence regions for θ*. We give Logit–Normal generalized linear mixed model examples, calculated using an R package.
Publié le : 2007-07-14
Classification:
Asymptotic theory,
Monte Carlo,
maximum likelihood,
generalized linear mixed model,
empirical process,
model misspecification,
62F12,
65C05
@article{1185303995,
author = {Sung, Yun Ju and Geyer, Charles J.},
title = {Monte Carlo likelihood inference for missing data models},
journal = {Ann. Statist.},
volume = {35},
number = {1},
year = {2007},
pages = { 990-1011},
language = {en},
url = {http://dml.mathdoc.fr/item/1185303995}
}
Sung, Yun Ju; Geyer, Charles J. Monte Carlo likelihood inference for missing data models. Ann. Statist., Tome 35 (2007) no. 1, pp. 990-1011. http://gdmltest.u-ga.fr/item/1185303995/