Bootstrap methods for bias correction and confidence interval estimation for nonlinear quantile regression of longitudinal data
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.
Keywords: autocorrelated errors; bias reduction; dependent errors; median regression; panel data; repeated measurements
Document Type: Research Article
Affiliations: Centre for Clinical Research VasterĂ¥s, Uppsala University, Central Hospital, VasterĂ¥s, Sweden
Publication date: 01 October 2009
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content