The Hat Matrix in Regression and ANOVA

作者：David C. Hoaglin, Roy E. Welsch · 发表于：The American Statistician · 年份：1978 · DOI：10.1080/00031305.1978.10479237 · 被引用次数：859 · 研究领域：Advanced Statistical Methods and Models、Statistical and numerical algorithms、Spectroscopy and Chemometric Analyses

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.