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- W2616451714 abstract "Robust regression procedures are designed to reduce the influence of outliers and influential points by reducing the weights given to large residuals. This can be done by the use of M-estimators, the class of estimators that minimizes some function of the residuals other than the sum of squares. M-estimators can be implemented as iteratively reweighted least squares, in which each observation’s weight depends on the corresponding residual. Each of these estimators requires the analyst to specify a certain tuning constant. The method of cross-validatory choice is used to select an optimal value of the tuning constant c for use with the weight of function'. The procedure selects that value of c which minimizes the median of the absolute predicted residuals (PRM). A resistant fit to initiate the iterative procedure is obtained by using Andrews’ method of regression by medians. Two examples are presented." @default.
- W2616451714 created "2017-05-26" @default.
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- W2616451714 date "1992-06-30" @default.
- W2616451714 modified "2023-09-26" @default.
- W2616451714 title "CROSS-VALIDATION IN ROBUST REGRESSION" @default.
- W2616451714 doi "https://doi.org/10.25211/jeas.v11i1.903" @default.
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