Prediction in linear mixed models

By Sue J. Welham, Brian R. Cullis, Beverley J. Gogel, A. R. Gilmour, Robin Thompson. 2004

112 citations recorded by the source corpus.

Subfields: Environmental Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/63f6919725e7892b7a9c52c6351ca26a8328e14b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. R. M. Lark
2. Sue J. Welham
3. Robin Thompson
4. Brian R. Cullis
5. Michael L. Stein
6. Richard Webster
7. Margaret A. Oliver
8. Hao Zhang

In Management Science and Operations Research

1. R. M. Lark
2. Brian R. Cullis
3. Sue J. Welham
4. Robin Thompson
5. Beverley J. Gogel
6. A. R. Gilmour
7. Michael L. Stein
8. Alison B. Smith

Citations in the corpus

• On spatial prediction of soil properties in the presence of a spatial trend: the empirical best linear unbiased predictor (E‐BLUP) with REML (2006, methodology): “Welham et al. (2004) and Gilmour et al. (2004) have recently shown how mixed # 2006 Silsoe Research Institute, European Journal of Soil Science, 57, 787–799 models should be used for prediction.”
• ASREML user guide release 1.0 (2002, background): “A formal description can be found in Gilmour et al. (2004) and Welham et al. (2004).”
• Kriging a soil variable with a simple nonstationary variance model (2009, background): “The approach has been taken up by statisticians (Welham et al. 2004) and environmental scientists (e.”