Bootstrap methods for bias correction and confidence interval estimation for nonlinear quantile regression of longitudinal data

DOI10.1080/00949650802221180zbMath1179.62060OpenAlexW2002034274WikidataQ61781650 ScholiaQ61781650MaRDI QIDQ3401434

Andreas Karlsson

Publication date: 29 January 2010

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-6939

zbMATH Keywords

bias reductionpanel datarepeated measurementsautocorrelated errorsmedian regressiondependent errors

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Parametric tolerance and confidence regions (62F25) General nonlinear regression (62J02) Bootstrap, jackknife and other resampling methods (62F40) Nonparametric statistical resampling methods (62G09)

Related Items (4)

Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies ⋮ ASYMPTOTIC THEORY FOR NONLINEAR QUANTILE REGRESSION UNDER WEAK DEPENDENCE ⋮ Random Weighting Estimation of Confidence Intervals for Quantiles ⋮ Local linear estimate of the functional expectile regression

Cites Work

• On marginal estimation in a semiparametric model for longitudinal data with time-independent covariates
• Quantile regression for longitudinal data
• Quasi-Likelihood for Median Regression Models
• Regression Quantiles
• Quantile Regression Methods for Longitudinal Data with Drop-outs: Application to CD4 Cell Counts of Patients Infected with the Human Immunodeficiency Virus
• Nonlinear Quantile Regression Estimation of Longitudinal Data

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