Monte carlo likelihood inference for missing data models
By Y. Sung, Charles J. Geyer. 2007
51 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/84bca2ff17f3e46bb6272a3bab002662467a0235
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Guido Masarotto
- Cristiano Varin
- Chandra R. Bhat
- A. C. Davison
- Mathieu Ribatet
- Simone A. Padoan
- Xanthi Pedeli
- Dimitris Karlis
In Statistics and Probability
- Cristiano Varin
- Guido Masarotto
- Chandra R. Bhat
- A. C. Davison
- Mathieu Ribatet
- Simone A. Padoan
- Nancy Reid
- David Firth
Citations in the corpus
- Gaussian Copula Marginal Regression (2012, background): “Examples include Markov chain Monte Carlo [60], Monte Carlo expectation-maximization [5] and importance sampling [51] algorithms.”
- A unified framework for multilevel uncertainty quantification in Bayesian inverse problems (2016, unknown): “based on importance sampling, can be applied in similar fashion [55, 56].”