TY - JOUR
T1 - An approximate EM algorithm for maximum likelihood estimation in generalized linear mixed models
AU - Xiang, Liming
AU - Tse, Siu-Keung
PY - 2003/8
Y1 - 2003/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0041883771&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-0041883771&origin=recordpage
U2 - 10.1081/SAC-120017862
DO - 10.1081/SAC-120017862
M3 - RGC 21 - Publication in refereed journal
SN - 0361-0918
VL - 32
SP - 787
EP - 798
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
IS - 3
ER -