摘要: Least squares estimators of regression coefficients can be overly sensitive to violations of certain error assumptions; e.g., outliers in the response variable. One solution to the presence of outliers in a data base is to apply u... 展开 Least squares estimators of regression coefficients can be overly sensitive to violations of certain error assumptions; e.g., outliers in the response variable. One solution to the presence of outliers in a data base is to apply univariate robust estimation procedures to the residuals of estimated models. Equally problematic as outliers among the response variable are outliers or aberrant values for the predictor variables. Extreme values on individual predictor variables or an unusual combination of predictor variable values for a few observational units can distort least squares estimators even if the error assumptions are valid. This article discusses robust regression procedures, with special emphasis on techniques which are resistant to extreme predictor variable values. (Author) 收起