Please use this identifier to cite or link to this item:
|Title:||Three ways of implementing the EM algorithm when parameters are not identifiable|
|Authors:||Yung Cheung Kuk, A. |
So Kuen Chan, J.
Monte Carlo methods
Multivariate order statistics models
Multivariate probit regression
|Source:||Yung Cheung Kuk, A.,So Kuen Chan, J. (2001). Three ways of implementing the EM algorithm when parameters are not identifiable. Biometrical Journal 43 (2) : 207-218. ScholarBank@NUS Repository.|
|Abstract:||We consider situations where the incomplete nature of the observed data causes identifiability problem. Rather than imposing identifiability constraints on the parameters and then implement the EM algorithm subject to these constraints, we argue that for certain problems, an easier option is to ignore the constraints during the M-steps of the EM procedure. We also suggest a way of carrying out constrained maximization approximately by using COX and WERMUTH'S (1990) method for approximating the constrained maximizers from the unconstrained ones at each M-step. The simplicity and validity of the unconstrained EM procedure are demonstrated using three examples involving bivariate probit regression, multivariate normal order statistics model and the multinominal distribution. Potential applications to more complicated models are also outlined.|
|Source Title:||Biometrical Journal|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Mar 10, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.