The rigidness in accepting predictors by GCV f is adjustable GCV f is a natural generalization of GCV.įor example, GCV f is designed so that the possibility of erroneous identification of linear relationships is 5 percent when all predictors have no linear relationships with the target variable. To take this possibility into account, a new statistics “ GCV f” (“ f”stands for “flexible”) is suggested. This is because GCV estimates prediction error, but does not control the probability of selecting irrelevant predictors of the target variable. Predictors of a multiple linear regression equation selected by GCV (Generalized Cross Validation) may contain undesirable predictors with no linear functional relationship with the target variable, but are chosen only by accident. Myers r 1990 classical and modern regression with.
Myers R 1990 Classical and modern regression with applications 2 nd ed Boston from ACCOUNTING 123 at University of Economics Ho Chi Minh City.pdf. Download classical and modern regression with applications book by (PDF, ePub, Mobi) Books classical and modern regression with applications book by (PDF, ePub, Mobi). Download Classical And Modern Regression With Applications pdf.
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