Akaike's Information Criterion in Generalized Estimating Equations

@article{Pan2001AkaikesIC,
  title={Akaike's Information Criterion in Generalized Estimating Equations},
  author={Wei Pan},
  journal={Biometrics},
  year={2001},
  volume={57}
}
  • W. Pan
  • Published 1 March 2001
  • Mathematics
  • Biometrics
Summary. Correlated response data are common in biomedical studies. Regression analysis based on the generalized estimating equations (GEE) is an increasingly important method for such data. However, there seem to be few model‐selection criteria available in GEE. The well‐known Akaike Information Criterion (AIC) cannot be directly applied since AIC is based on maximum likelihood estimation while GEE is nonlikelihood based. We propose a modification to AIC, where the likelihood is replaced by… 

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References

SHOWING 1-10 OF 36 REFERENCES

A caveat concerning independence estimating equations with multivariate binary data.

TLDR
This paper considers logistic regression models for multivariate binary responses, where the association between the responses is largely regarded as a nuisance characteristic of the data, and considers the estimator based on independence estimating equations (IEE), which assumes that the responses are independent.

Goodness-of-fit tests for GEE modeling with binary responses.

TLDR
This work proposes model-based and robust (empirically corrected) goodness-of-fit tests for GEE modeling with binary responses based on partitioning the space of covariates into distinct regions and forming score statistics that are asymptotically distributed as chi-square random variables with the appropriate degrees of freedom.

Models for longitudinal data: a generalized estimating equation approach.

TLDR
This article discusses extensions of generalized linear models for the analysis of longitudinal data in which heterogeneity in regression parameters is explicitly modelled and uses a generalized estimating equation approach to fit both classes of models for discrete and continuous outcomes.

Longitudinal data analysis using generalized linear models

SUMMARY This paper proposes an extension of generalized linear models to the analysis of longitudinal data. We introduce a class of estimating equations that give consistent estimates of the

A cautionary note on inference for marginal regression models with longitudinal data and general correlated response data

Inference for cross-sectional models using longitudinal data, can be accomplished with generalized estimating equations (Zeger and Liang, 1992). We show that either a diagonal working covariance

Longitudinal data analysis for discrete and continuous outcomes.

TLDR
A class of generalized estimating equations (GEEs) for the regression parameters is proposed, extensions of those used in quasi-likelihood methods which have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as the authors often expect.

Estimating Logistic Regression Parameters for Bivariate Binary Data

SUMMARY Consider bivariate binary data, with possibly different covariates for each marginal binary observation. Suppose that the correlation between paired observations is a nuisance, and the

Approximate likelihood ratios for general estimating functions

SUMMARY The method of estimating functions (Godambe, 1991) is commonly used when one desires to conduct inference about some parameters of interest but the full distribution of the observations is

Quasi-likelihood functions, generalized linear models, and the Gauss-Newton method

SUMMARY To define a likelihood we have to specify the form of distribution of the observations, but to define a quasi-likelihood function we need only specify a relation between the mean and variance

Subset Selection in Regression

OBJECTIVES Prediction, Explanation, Elimination or What? How Many Variables in the Prediction Formula? Alternatives to Using Subsets 'Black Box' Use of Best-Subsets Techniques LEAST-SQUARES