Median regression for longitudinal data

Xuming He, Bo Fu, Wing K. Fung

    Research output: Contribution to journalArticlepeer-review


    We review and compare three estimators of median regression in linear models with longitudinal data. The estimators are constructed based on well-known ideas of weighting, decorrelating, and the working assumption of independence. Both asymptotic efficiency calculations and finite-sample Monte Carlo studies are used to assess the performance of these estimators. We find that their relative performances depend on the nature of covariates. The estimator under the working assumption of independence is computationally simple and yet has good relative performance when the covariates are invariant over time or when the within-subject correlations are small. Its relative performance in finite samples is also found to be more favourable than suggested by the asymptotic comparisons. Copyright © 2003 John Wiley & Sons, Ltd.
    Original languageEnglish
    Pages (from-to)3655-3669
    Number of pages14
    JournalStatistics in medicine
    Issue number23
    Publication statusPublished - 15 Dec 2003


    • Efficiency
    • Estimating equation
    • Longitudinal data
    • Median regression
    • Mixed model
    • Robustness


    Dive into the research topics of 'Median regression for longitudinal data'. Together they form a unique fingerprint.

    Cite this