The derivation of BLUP, ML, REML estimation methods for generalised linear mixed models
Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
Author(s)
Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 2963-2980 |
| Journal / Publication | Communications in Statistics - Theory and Methods |
| Volume | 24 |
| Issue number | 12 |
| Publication status | Published - 1 Jan 1995 |
| Externally published | Yes |
Link(s)
Abstract
This paper presents a unified derivation of BLUP, ML and REML estimation procedures for normally distributed response variables with possibly correlated random components occurring in the mixed model for the mean. The theory is extended to generalised linear mixed models, where the response variable is not necessarily normally distributed but the model may be fitted using a penalised quasi-likelihood approach which mirrors the development in normal theory models. The method is applied to binomially distributed response variables with logit link to a mixed model containing a random component distributed as an AR(1) process. © 1995, Taylor & Francis Group, LLC. All rights reserved.
Research Area(s)
- generalised mixed models, residual maximum likelihood
Citation Format(s)
The derivation of BLUP, ML, REML estimation methods for generalised linear mixed models. / McGilchrist, C. A.; Yau, K. K W.
In: Communications in Statistics - Theory and Methods, Vol. 24, No. 12, 01.01.1995, p. 2963-2980.Research output: Journal Publications and Reviews (RGC: 21, 22, 62) › 21_Publication in refereed journal › peer-review
