A bias-adjusted estimator in quantile regression for clustered data
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Quantile regression models with random effects are useful for studying associations between covariates and quantiles of the response distribution for clustered data. Parameter estimation is examined for a class of mixed-effects quantile regression models, with focus on settings with many but small clusters. The main contributions are the following: (i) documenting that existing methods may lead to severely biased estimators for fixed effects parameters; (ii) proposing a new two-step estimation methodology where predictions of the random effects are first computed by a pseudo likelihood approach (the LQMM method) and then used as offsets in standard quantile regression; (iii) proposing a novel bootstrap sampling procedure in order to reduce bias of the two-step estimator and compute confidence intervals. The proposed estimation and associated inference is assessed numerically through rigorous simulation studies and applied to an AIDS Clinical Trial Group (ACTG) study.
Originalsprog | Engelsk |
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Tidsskrift | Econometrics and Statistics |
Vol/bind | 23 |
Sider (fra-til) | 165-186 |
Antal sider | 22 |
DOI | |
Status | Udgivet - 2022 |
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ID: 291755082