Originalsprog | Engelsk |
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Titel | Wiley StatsRef : Statistics Reference Online |
Redaktører | Marie Davidian, Ron S. Kenett, Nicholas T. Longford, Geert Molenberghs, Walter Piegorsch, Fabrizio Ruggeri |
Udgivelsessted | Hoboken, NJ |
Forlag | Wiley |
Publikationsdato | 15 maj 2018 |
ISBN (Elektronisk) | 9781118445112 |
DOI | |
Status | Udgivet - 15 maj 2018 |
Abstract
The expectation maximization (EM) algorithm is a useful tool for finding the maximum likelihood estimator (MLE) in incomplete data problems. In some problems, however, the E step (and/or the M step) of the algorithm may be difficult to implement. Here, the stochastic EM algorithm can provide a useful alternative by replacing the E step of the EM algorithm with a fixed number of simulations, turning the M step into a maximization of the complete data log‐likelihood. The output of the stochastic EM algorithm forms a Markov chain that under sufficient regularity conditions is ergodic with an asymptotically normal invariant distribution. Draws from the invariant distribution form a consistent asymptotically normal estimator of the unknown parameters.
Emneord
- Incomplete data
- EM algorithm
- Imputation
- Simulation
- Estimation