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expectreg

swMATH ID: 43559
Software Authors: Barry, Amadou; Oualkacha, Karim; Charpentier, Arthur
Description: A new GEE method to account for heteroscedasticity using asymmetric least-square regressions. Generalized estimating equations GEE 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 GEE with the asymmetric least squares (expectile) regression to derive a new class of estimators, which we call generalized expectile estimating equations (GEEE). The GEEE 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 GEEE 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.
Homepage: https://arxiv.org/abs/1810.09214
Source Code:  https://github.com/amadoudiogobarry/expectgee
Dependencies: R
Keywords: R package expectgee; Generalized estimating equations; Expectile regression; quantile regression; working correlation; cluster data; longitudinal data
Related Software: expectgee; R; quantreg; erfe; rqpd; expectreg; erboost; copula; SAS/STAT; copula; hidetify; geepack; SAS
Cited in: 2 Documents

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