%0 Journal Article %1 lumley_weighted_1999 %A Lumley, Thomas %A Heagerty, Patrick %B Journal of the Royal Statistical Society Series B %D 1999 %J Journal of the Royal Statistical Society. Series B (Statistical Methodology) %K GEE, analysis autocorrelation, bootstrap, temporal time-series %N 2 %P 459 -- 477 %R 10.1111/1467-9868.00187 %T Weighted Empirical Adaptive Variance Estimators for Correlated Data Regression %U http://www.jstor.org/stable/2680652 %V 61 %X Estimating equations based on marginal generalized linear models are useful for regression modelling of correlated data, but inference and testing require reliable estimates of standard errors. We introduce a class of variance estimators based on the weighted empirical variance of the estimating functions and show that an adaptive choice of weights allows reliable estimation both asymptotically and by simulation in finite samples. Connections with previous bootstrap and jackknife methods are explored. The effect of reliable variance estimation is illustrated in data on health effects of air pollution in King County, Washington.

@article{lumley_weighted_1999, abstract = {Estimating equations based on marginal generalized linear models are useful for regression modelling of correlated data, but inference and testing require reliable estimates of standard errors. We introduce a class of variance estimators based on the weighted empirical variance of the estimating functions and show that an adaptive choice of weights allows reliable estimation both asymptotically and by simulation in finite samples. Connections with previous bootstrap and jackknife methods are explored. The effect of reliable variance estimation is illustrated in data on health effects of air pollution in King County, Washington.}, added-at = {2017-01-09T13:57:26.000+0100}, author = {Lumley, Thomas and Heagerty, Patrick}, biburl = {https://www.bibsonomy.org/bibtex/208cbaf97fa8286710a9f9902daec0b85/yourwelcome}, copyright = {Copyright 1999 The Royal Statistical Society}, doi = {10.1111/1467-9868.00187}, interhash = {808fe8ab63284cf72469c714f79b59a1}, intrahash = {08cbaf97fa8286710a9f9902daec0b85}, issn = {1369-7412}, journal = {Journal of the Royal Statistical Society. Series B (Statistical Methodology)}, keywords = {GEE, analysis autocorrelation, bootstrap, temporal time-series}, language = {English}, month = jan, number = 2, pages = {459 -- 477}, series = {Journal of the {Royal} {Statistical} {Society} {Series} {B}}, timestamp = {2017-01-09T14:01:11.000+0100}, title = {Weighted {Empirical} {Adaptive} {Variance} {Estimators} for {Correlated} {Data} {Regression}}, url = {http://www.jstor.org/stable/2680652}, urldate = {2016-07-05}, volume = 61, year = 1999 }