Efron, B. (1978), Regression and ANOVA with zero-one data: measures of residual variation, Journal of the American Statistical Association, 73, 113-121. Home/Search Document Not in Database Summary Related Articles Check |
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Pseudo-R Measures for Some Common Limited Dependent Variable.. - Veall, Zimmermann (1996) (Correct)
.... cov(Y,H) 2 var(Y) var(H) var(H) var(Y) R 2 L 1 j N i 1 (Y i H i ) 2 j N i 1 (Y i Y) 2 13 Table 1 Cont d Correlation Class Neter and Maynes (1970) Morrison (1972) Goldberger (1973) and Efron (1978) Lave (1970) 14 The McFadden R is probably the most popular Pseudo R and for example is the only 2 2 MF Pseudo R provided by the computer package STATA (1995) The various expressions for it 2 in Table 1 show how it is ....
Efron B. (1978): "Regression and ANOVA with Zero-One Data: Measures of Residual Variation", Journal of the American Statistical Association 73, pp. 113-121.
Key Words: - Ceres Plot (Correct)
....data are fitted with a log transformed model, the resulting residual and partial residual plots show virtually identical cubic forms. For this example, all four plots examined suggest that a cubic logistic regression model provides a better fit than a linear one. This finding is consistent with Efron s (1978, 1986) conclusions regarding the effect of rainfall on the proportion of subjects testing positive. Example 6. Assay on Insecticide and Synergist This data set is taken from McCullagh and Nelder (1989, p.385) and concerns the relationship between the number of grasshoppers Melanopus Sanguinipes ....
Efron, B.(1978). Regression and ANOVA with zero-one data: Measures of residual variation. J.
Error-Correcting Output Coding Corrects Bias and Variance - Kong, Dietterich (1995) (78 citations) (Correct)
....is equal to the sum of the squared error of the ideal voted hypothesis and the variance of each individual hypothesis with respect to this ideal voted hypothesis. Extending notions of bias and variance to classification problems is not straightforward, and many alternative approaches are possible (Efron, 1978). Our approach, which we have not previously seen, is based on idealized voting. Let A be a classification learning algorithm, and let f S i be the hypothesis produced by A when trained on training set S i . For a test set example x, f S i (x) is equal to one of k possible classes fc 0 ; ....
Efron, B. (1978). Regression and ANOVA with zeroone data: Measures of residual variation. J.
Bayesian Residual Analysis For Binary Response Regression.. - Jim Albert Bowling (1996) (5 citations) (Correct)
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Efron, B. (1978), Regression and ANOVA with zero-one data: measures of residual variation, Journal of the American Statistical Association, 73, 113-121.
Bayesian Residual Analysis For Binary Response Regression Models - Jim Albert (1996) (5 citations) (Correct)
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Efron, B. (1978). Regression and ANOVA with zero-one data: measures of residual variation. J. Am. Statist. Assoc. 73, 113-121.
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