Regression Diagnostic under Model Misspecification

Li-Chu Chien and Tsung-Shan Tsou

Journal of Applied Statistics, 2007, vol. 34, issue 5, 563-575

Abstract: 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.

Keywords: Influential diagnostic; robust likelihood; robust normal regression; DFBETAS; DFFITS; Cook's distance (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1080/02664760701235014

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