We introduce a generalized bootstrap technique for estimators obtained by solving estimating equations. Some special cases of this generalized bootstrap are the classical bootstrap of Efron, the delete-d jackknife and variations of the Bayesian bootstrap. The use of the proposed technique is discussed in some examples. Distributional consistency of the method is established and an asymptotic re...

We consider the marginal models of Liang and Zeger [Biometrika 73 (1986) 13–22] for the analysis of longitudinal data and we develop a theory of statistical inference for such models. We prove the existence , weak consistency and asymptotic normality of a sequence of estimators defined as roots of pseudo-likelihood equations. 1. Introduction. Longitudinal data sets arise in biostatistics and li...

We consider estimation in a semiparametric generalized linear model for clustered data using estimating equations. Our results apply to the case where the number of observations per cluster is Ž nite, whereas the number of clusters is large. The mean of the outcome variable Œ is of the form g4Œ5 D X‚C ˆ4T 5, where g4¢5 is a link function, X and T are covariates, ‚ is an unknown parameter vector...

We study generalized linear latent variable models without requiring a distributional assumption of the latent variables. Using a geometric approach, we derive consistent semiparametric estimators. We demonstrate that these models have a property which is similar to that of a sufficient complete statistic, which enables us to simplify the estimating procedure and explicitly to formulate the sem...

BACKGROUND In neonatal trials of pre-term or low-birth-weight infants, twins may represent 10-20% of the study sample. Mixed-effects models and generalized estimating equations are common approaches for handling correlated continuous or binary data. However, the operating characteristics of these methods for mixes of correlated and independent data are not well established. METHODS Simulation...

We consider the penalized generalized estimating equations (GEEs) for analyzing longitudinal data with high-dimensional covariates, which often arise in microarray experiments and large-scale health studies. Existing high-dimensional regression procedures often assume independent data and rely on the likelihood function. Construction of a feasible joint likelihood function for high-dimensional ...

Elevated plasma levels of apolipoproteins A1 (apoA1) and B (apoB) are important protective factors and risk factors, respectively, for atherosclerosis and coronary heart disease. It is well known that both apoA1 and apoB reveal strong familial aggregation. Our goal was to investigate whether exogenous variables influence these associations. We used marginal regression models for the mean and as...

This talk discusses the problem of calculating power in generalized estimating equation (GEE) settings that arise in biomedical studies involving clustered or correlated data (e.g., longitudinal studies and sibling studies). Existing approaches [e.g., Liu and Liang (1997) and Shih (1997)] approximate the power based on fixed alternatives. A more rigorous and potentially more accurate approach, ...

Many large-scale longitudinal imaging studies have been or are being widely conducted to better understand the progress of neuropsychiatric and neurodegenerative disorders and normal brain development. The goal of this article is to develop a multiscale adaptive generalized estimation equation (MAGEE) method for spatial and adaptive analysis of neuroimaging data from longitudinal studies. MAGEE...