An approximate EM algorithm for maximum likelihood estimation in generalized linear mixed models
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 787-798 |
| Journal / Publication | Communications in Statistics Part B: Simulation and Computation |
| Volume | 32 |
| Issue number | 3 |
| Publication status | Published - Aug 2003 |
Link(s)
Abstract
This article considers the maximum likelihood estimation in generalized linear mixed models (GLMMs). Using results from number theory, we propose a numerical EM algorithm to find the maximum likelihood estimates in GLMMs. Details of the steps required by applying the proposed numerical method to the E-step of the EM algorithm are outlined. The performance and efficiency of the proposed method are compared to some commonly used methods discussed in the literature.
Citation Format(s)
An approximate EM algorithm for maximum likelihood estimation in generalized linear mixed models. / Xiang, Liming; Tse, Siu-Keung.
In: Communications in Statistics Part B: Simulation and Computation, Vol. 32, No. 3, 08.2003, p. 787-798.
In: Communications in Statistics Part B: Simulation and Computation, Vol. 32, No. 3, 08.2003, p. 787-798.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
