Monte Carlo Likelihood Inference for Missing Data Models
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 observe...
| Veröffentlicht in: | The Annals of Statistics. - Institute of Mathematical Statistics. - 35(2007), 3, Seite 990-1011 |
|---|---|
| 1. Verfasser: | |
| Weitere Verfasser: | |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2007
|
| Zugriff auf das übergeordnete Werk: | The Annals of Statistics |
| Schlagworte: | Asymptotic theory Monte Carlo Maximum likelihood Generalized linear mixed model Empirical process Model misspecification Mathematics Information science Philosophy Physical sciences |
| Online verfügbar |
Volltext |