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

Shuangzhe LIU, Victor Leiva, Tiefeng Ma

    Research output: Contribution to journalArticle

    9 Citations (Scopus)

    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
    DOIs
    Publication statusPublished - 2016

    Fingerprint Dive into the research topics of 'Influence diagnostic analysis in the possibly heteroskedastic linear model with exact restrictions'. Together they form a unique fingerprint.

    Cite this