Weighted empirical adaptive variance estimators for correlated data regression

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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.

Keywords: Bootstrap; Estimating equations; Generalized linear model; Jackknife; Time series

Document Type: Original Article

DOI: http://dx.doi.org/10.1111/1467-9868.00187

Affiliations: University of Washington, Seattle, USA

Publication date: April 1, 1999

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