Accommodating Outliers and Nonlinearity in Decision Models.

This paper describes and compares six procedures that can be used in a regression model to adjust for outliers in the data and nonlinearities in the relationship between the dependent and independent variables. The data accommodation procedures are: (1) no-adjustment; (2) winsorizing; (3) trimming; (4) regression on ranks; (5) nonlinear regression; and (6) piecewise linear regression. The results show that the choice of data accommodation procedure has a major impact on the predictive ability and coefficient estimates of the regression model. The winsorizing and ranking procedures produce a regression model that fits the data well and has a low level of prediction error. [ABSTRACT FROM AUTHOR]