The Hat Matrix in Regression and ANOVA

1 February 1978

journal article
research article
Published by Taylor & Francis in The American Statistician

Vol. 32 (1) , 17-22
https://doi.org/10.1080/00031305.1978.10479237

Abstract

In least-squares fitting it is important to understand the influence which a data y value will have on each fitted y value. A projection matrix known as the hat matrix contains this information and, together with the Studentized residuals, provides a means of identifying exceptional data points. This approach also simplifies the calculations involved in removing a data point, and it requires only simple modifications in the preferred numerical least-squares algorithms.

Keywords

This publication has 7 references indexed in Scilit:

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