A BOUNDED INFLUENCE, HIGH BREAKDOWN, EFFICIENT REGRESSION ESTIMATOR

• 成果类型: Article
• 署名作者: COAKLEY, CW; HETTMANSPERGER, TP
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.2307/2290776
• 发表日期: 1993
• 页码: 872-880
• 关键词: point; scale

摘要

We consider the multiple linear regression model y(i) = x(i)'beta + epsilon(i), i = 1, 2, . . . , n, with random carriers and focus on the estimation of beta. This article's main contribution is to present an estimator that is affine, regression, and scale equivariant; has both a high breakdown point and a bounded influence function; and has an asymptotic efficiency greater than .95 versus least squares under Gaussian errors. We give conditions under which the estimator-a one-step general M estimator that uses Schweppe weights and is based on a high breakdown initial estimator-satisfies these properties. The major conditions necessary for the estimator are (a) it must be based on a square-root n-consistent initial estimator with a 50% breakdown point, and (b) it must be based on a psi function that is odd, bounded, and strictly increasing. The advantage of this estimator over previous approaches is that it does not downweight high leverage points without first considering how they fit the bulk of the data. Methods of computing diagnostics and constructing Wald-type tests about beta are given. We illustrate the features of the estimator on a data set with two regressors, showing how a good leverage point is not downweighted, whereas a bad leverage point is downweighted.