TY - JOUR
T1 - Application of 'delete = replace' to deletion diagnostics for variance component estimation in the linear mixed model
AU - Haslett, John
AU - Dillane, Dominic
PY - 2004
Y1 - 2004
N2 - 'Delete = replace' is a powerful and intuitive modelling identity. This paper extends previous work by stating and proving the identity in more general terms and extending its application to deletion diagnostics for estimates of variance components obtained by restricted maximum likelihood estimation for the linear mixed model. We present a new, fast, transparent and approximate computational procedure, arising as a by-product of the fitting process. We illustrate the effect of the deletion of individual observations, of 'subjects' and of arbitrary subsets. Central to the identity and its application is the conditional residual.
AB - 'Delete = replace' is a powerful and intuitive modelling identity. This paper extends previous work by stating and proving the identity in more general terms and extending its application to deletion diagnostics for estimates of variance components obtained by restricted maximum likelihood estimation for the linear mixed model. We present a new, fast, transparent and approximate computational procedure, arising as a by-product of the fitting process. We illustrate the effect of the deletion of individual observations, of 'subjects' and of arbitrary subsets. Central to the identity and its application is the conditional residual.
KW - Conditional residuals
KW - Generalized least squares
KW - Leverage
KW - Restricted maximum likelihood
UR - http://www.scopus.com/inward/record.url?scp=1042302535&partnerID=8YFLogxK
U2 - 10.1046/j.1369-7412.2003.05211.x
DO - 10.1046/j.1369-7412.2003.05211.x
M3 - Article
AN - SCOPUS:1042302535
SN - 1369-7412
VL - 66
SP - 131
EP - 143
JO - Journal of the Royal Statistical Society. Series B: Statistical Methodology
JF - Journal of the Royal Statistical Society. Series B: Statistical Methodology
IS - 1
ER -