论文信息 - Augmented Robust Estimators

Augmented Robust Estimators

A method is presented based on augmented data sets for combining biased and robust regression techniques. The estimates are constrained robust estimates, using an appropriately chosen ridge, shrinkage or principal components constraint. Examples are provided to illustrate the ability of the procedure to shrink the estimated coefficients and to automatically detect and discount the effects of observations with large random error components.

D. Montgomery | Ronald G. Arkin

[1] P. J. Huber. Robust Estimation of a Location Parameter , 1964 .

[2] J. W. Gorman,et al. Selection of Variables for Fitting Equations to Data , 1966 .

[3] Donald W. Marquaridt. Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation , 1970 .

[4] A. E. Hoerl,et al. Ridge Regression: Applications to Nonorthogonal Problems , 1970 .

[5] J. W. Gorman,et al. Fitting Equations to Data. , 1973 .

[6] P. Holland. Weighted Ridge Regression: Combining Ridge and Robust Regression Methods , 1973 .

[7] J. T. Webster,et al. Latent Root Regression Analysis , 1974 .

[8] D. F. Andrews,et al. Robust Estimates of Location: Survey and Advances. , 1975 .

[9] C. J. Lawrence. Robust estimates of location : survey and advances , 1975 .

[10] R. R. Hocking,et al. A Class of Biased Estimators in Linear Regression , 1976 .

[11] R. R. Hocking. The analysis and selection of variables in linear regression , 1976 .

[12] Colin L. Mallows,et al. Two Diagnostic Displays for Robust Regression Analysis , 1977 .

[13] J. Ramsay. A Comparative Study of Several Robust Estimates of Slope, Intercept, and Scale in Linear Regression , 1977 .

[14] J. Dennis,et al. Techniques for nonlinear least squares and robust regression , 1978 .