Maximum Likelihood from Incomplete Data via the EM Algorithm
| Title: | Maximum Likelihood from Incomplete Data via the EM Algorithm |
| Author: |
Dempster, Arthur P.; Laird, Nan M.; Rubin, Donald B.
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Dempster, Arthur P, Nan M. Laird, and Donald B. Rubin. 1976. Maximum Likelihood from Incomplete Data via the EM Algorithm. Paper presented at the Royal Statistical Society at a meeting organized by the Research Section, December 8, 1976. |
| Access Status: | At the direction of the depositing author this work is not currently accessible through DASH. |
| Full Text & Related Files: |
Rubin_MaxLikelihood.pdf (5.520Mb; PDF) 
|
| Abstract: | A broadly applicable algorithm for computing maximum likelihood estimates from incomplete data is presented at various levels of generality. Theory showing the monotone behavior of the likelihood and convergence of the algorithm is derived. Many examples are sketched, including missing value situations, applications to grouped, censored or truncated data, finite mixture models, variance component estimation, hyperparameter estimation, iteratively reweighted least squares and factor analysis. |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:3426318 |
This item appears in the following Collection(s)
-
FAS Scholarly Articles [5128]
Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
Contact administrator regarding this item (to report mistakes or request changes)
