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- W604822984 abstract "This paper examines the use of bootstrapping for bias correction and calculation of confidence intervals (CIs) for a weighted nonlinear quantile regression estimator adjusted to the case of longitudinal data. Different weights and types of CIs are used and compared by computer simulation using a logistic growth function and error terms following an AR(1) model. The results indicate that bias correction reduces the bias of a point estimator but fails for CI calculations. A bootstrap percentile method and a normal approximation method perform well for two weights when used without bias correction. Taking both coverage and lengths of CIs into consideration, a non-bias-corrected percentile method with an unweighted estimator performs best." @default.
- W604822984 created "2016-06-24" @default.
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- W604822984 date "2006-01-01" @default.
- W604822984 modified "2023-09-27" @default.
- W604822984 title "Estimation and Inference for Quantile Regression of Longitudinal Data : With Applications in Biostatistics" @default.
- W604822984 hasPublicationYear "2006" @default.
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