ROBUST ANALYSIS OF LONGITUDINAL DATA(Statistical Models for Biomedical Research)

    • Fu Bo
    • Department of Statistics and Actuarial Science, The University of Hong Kong
    • Fung Wing K.
    • Department of Statistics and Actuarial Science, The University of Hong Kong
    • He Xuming
    • Department of Statistics, University of Illinois

Abstract

Two robust methods for the analysis of longitudinal data are discussed. The marginal linear model with correlated observations within individuals is employed. We summarize literature on robust methods and suggest the modifications of Huggins' and Jung's estimates to simplify the procedure for the analysis of longitudinal data. The small sample behaviours of the two estimates are investigated under various situations by simulation. A real data set is analysed. This paper provides a useful reference for practitioners to the choice of robust methods in the analysis of longitudinal data.

Journal

Journal of the Japanese Society of Computational Statistics   [List of Volumes]

Journal of the Japanese Society of Computational Statistics 15(2), 81-87, 2003-06  [Table of Contents]

Japanese Society of Computational Statistics

References:  12

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Codes

  • NII Article ID (NAID) :
    110001235165
  • NII NACSIS-CAT ID (NCID) :
    AA10823693
  • Text Lang :
    ENG
  • Article Type :
    REV
  • ISSN :
    09152350
  • Databases :
    CJP  NII-ELS 

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