Influence diagnostic analysis in the possibly heteroskedastic linear model with exact restrictions

Shuangzhe Liu, Víctor Leiva, Tiefeng Ma, Alan Welsh

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

The local influence method has proven to be a useful and powerful tool for detecting influential observations on the estimation of model parameters. This method has been widely applied in different studies related to econometric and statistical modelling. We propose a methodology based on the Lagrange multiplier method with a linear penalty function to assess local influence in the possibly heteroskedastic linear regression model with exact restrictions. The restricted maximum likelihood estimators and information matrices are presented for the postulated model. Several perturbation schemes for the local influence method are investigated to identify potentially influential observations. Three real-world examples are included to illustrate and validate our methodology.

Original languageEnglish
Pages (from-to)227-249
Number of pages23
JournalStatistical Methods and Applications
Volume25
Issue number2
DOIs
StatePublished - 1 Jun 2016
Externally publishedYes

Keywords

  • Information matrix
  • Local influence
  • Restricted least-squares estimator
  • Restricted maximum likelihood estimator

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