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Contrasting Alternatives to Least Squares in Regression Using Diagnostics for Identifying Influential Data
Abstract:
The implicit assumptions associated with several alternative methods for estimating regression coefficients are identified using the regression diagnostic measures of leverage and influence. A formulation of these measures for use with the Ridge, Stein, principal component and equal weight estimators is presented and then used to contrast competing assumptions. Based on the implicit priors, situations are denoted where each of the alternatives can be expected to perform best.
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