Sensitivity analysis in linear regression / Samprit Chatterjee, Ali S. Hadi.

Includes bibliographical references (pages 301-307) and index.

The prediction matrix -- Role of variables in a regression equation -- Effects of an observation on a regression equation -- Assessing the influence of multiple observations -- Joint impact of a variable and an observation -- Assessing the effect of errors of measurements -- A study of model sensitivity by the generalized linear model approach -- Computational considerations -- Appendix: Summary of vector and matrix norms, proofs of three theorems -- references -- Index.

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Treats linear regression diagnostics as a tool for application of linear regression models to real-life data. Presentation makes extensive use of examples to illustrate theory. Assesses the effect of measurement errors on the estimated coefficients, which is not accounted for in a standard least squares estimate but is important where regression coefficients are used to apportion effects due to different variables. Also assesses qualitatively and numerically the robustness of the regression fit.

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