Regression diagnostic under model misspecification

Li Chu Chien, Tsung Shan Tsou

Research output: Contribution to journalArticlepeer-review


We propose two novel diagnostic measures for the detection of influential observations for regression parameters in linear regression. Traditional diagnostic statistics focus on the effect of deletion of data points either on parameter estimates, or on predicted values. A data point is regarded as influential by the new methods if its inclusion determines a significantly different likelihood function for the parameter of interest. The concerned likelihood function is asymptotically valid for practically all underlying distributions whose second moments exist.

Original languageEnglish
Pages (from-to)563-575
Number of pages13
JournalJournal of Applied Statistics
Issue number5
StatePublished - Jul 2007


  • Cook's distance
  • Influential diagnostic
  • Robust likelihood
  • Robust normal regression


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