Parametric robust regression analysis of contaminated data

Tsung Shan Tsou, Kuang Fu Cheng

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


In reality it is more often the case when part of data have been drawn from some distribution other than that we believe they have been drawn from. Traditional nonparametric or parametric statistics are insufficient for such contaminated data. Tsou [Tsou, T.-S. (2003). Parametric robust inferences for regression parameters under generalized linear models. Submitted] proposed parametric robust regression techniques that provide asymptotically valid inference for regression parameters so long as the true distributions that generate the data have finite second moments. It will be demonstrated that the novel robust regression tools fit well for the regression analysis of data generated from unknown and distinct families of distributions.

Original languageEnglish
Pages (from-to)1887-1898
Number of pages12
JournalCommunications in Statistics - Theory and Methods
Issue number8
StatePublished - Aug 2004


  • Contaminated data
  • Robust Likelihood
  • Robust gamma regression
  • Robust normal regression


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