Bootstrap Methods for Bias Correction and Confidence Interval Estimation for Nonlinear Quantile Regression of Longitudinal Data
2009 (English)In: Journal of Statistical Computation and Simulation, ISSN 0094-9655, E-ISSN 1563-5163, Vol. 79, no 10, 1205-1218 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2009. Vol. 79, no 10, 1205-1218 p.
autocorrelated errors, bias reduction, dependent errors, median regression, panel data, repeated measurements
Computer and Information Science
IdentifiersURN: urn:nbn:se:uu:diva-94966DOI: 10.1080/00949650802221180ISI: 000270155800003OAI: oai:DiVA.org:uu-94966DiVA: diva2:169003