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Diagnostic methods using residuals
Assessment of influence
Alternative approaches to influence
3 other sections not shown
added variable plot analysis Appendix A.2 appropriate approximation chapter cloud seeding data columns computed consider constant constructed variable contours correlation corresponding data set defined deleted denote depend diagnostic discussed eigenvalues eigenvectors elements ellipsoid errors example explanatory variables F-statistics Figure fitted model fitted values function i i i i i-th influence measures influential jet fighter data Kullback-Leibler divergence least squares estimate leukemia data linear least squares linear model linear regression log likelihood logistic regression matrix maximum likelihood estimate methods monotonic function nonadditivity nonlinear norm normal distribution obtained one-step estimator outlier outlying p-value pair parameters partial residual plot points potential power family predictive density Pregibon probability plot problem procedure reduced data relatively response robust estimate robust regression sample influence curve scale score statistic Section Studentized residuals subsets suggest transformation tree data variance vector versus