A new GEE method to account for heteroscedasticity using asymmetric least-square regressions

Generalized estimating equations are widely used to analyze longitudinal data; however, they are not appropriate for heteroscedastic data, because they only estimate regressor effects on the mean response - and therefore do not account for data heterogeneity. Here, we combine the with the asymmetric...

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Veröffentlicht in:Journal of applied statistics 2022-10, Vol.49 (14), p.3564-3590
Hauptverfasser: Barry, Amadou, Oualkacha, Karim, Charpentier, Arthur
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Sprache:eng
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Zusammenfassung:Generalized estimating equations are widely used to analyze longitudinal data; however, they are not appropriate for heteroscedastic data, because they only estimate regressor effects on the mean response - and therefore do not account for data heterogeneity. Here, we combine the with the asymmetric least squares (expectile) regression to derive a new class of estimators, which we call generalized expectile estimating equations . The model estimates regressor effects on the expectiles of the response distribution, which provides a detailed view of regressor effects on the entire response distribution. In addition to capturing data heteroscedasticity, the GEEE extends the various working correlation structures to account for within-subject dependence. We derive the asymptotic properties of the estimators and propose a robust estimator of its covariance matrix for inference (see our R package, github.com/AmBarry/expectgee ). Our simulations show that the GEEE estimator is non-biased and efficient, and our real data analysis shows it captures heteroscedasticity.
ISSN:0266-4763
1360-0532
DOI:10.1080/02664763.2021.1957789