Longitudinal data analysis using t-type regression

Xuming He, Hengjian Cui, Douglas G. Simpson

Research output: Contribution to journalArticlepeer-review


We consider a robust estimator of linear regression for longitudinal data by maximizing marginal likelihood of a scaled t-type error distribution. The marginal likelihood can also be applied to the de-correlated response when the within-subject correlation can be consistently estimated from an initial estimate of the model based on the working assumption of independence. While the t-distributed errors can be motivated from a latent hierarchical model as an extension of Gaussian mixed models, our estimators have asymptotic normal distributions for a wider class of error distributions. The estimators have bounded influence functions and can achieve positive breakdown points regardless of the dimension of the covariates.

Original languageEnglish (US)
Pages (from-to)253-269
Number of pages17
JournalJournal of Statistical Planning and Inference
Issue number1-2
StatePublished - May 1 2004


  • Asymptotic normality
  • Correlation
  • Longitudinal data
  • M-estimator
  • One-step estimator
  • t-type regression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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