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Variable Selection For 1d Regression Models, David J. Olive, Douglas M. Hawkins
Variable Selection For 1d Regression Models, David J. Olive, Douglas M. Hawkins
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Variable selection, the search for j relevant predictor variables from a group of p candidates, is a standard problem in regression analysis. The class of 1D regression models is a broad class that includes generalized linear models. We show that existing variable selection algorithms, originally meant for multiple linear regression and based on ordinary least squares and Mallows’ Cp, can also be used for 1D models. Graphical aids for variable selection are also provided.
Applications Of Robust Distances For Regression, David J. Olive
Applications Of Robust Distances For Regression, David J. Olive
Articles and Preprints
The DD plot, introduced by Rousseeuw and Van Driessen (1999), is a plot of classical vs robust Mahalanobis distances: MDi vs RDi. The DD plot can be used as a diagnostic for multivariate normality and elliptical symmetry, and to assess the success of numerical transformations towards elliptical symmetry. In the regression context, many procedures can be adversely affected if strong nonlinearities are present in the predictors. Even if strong nonlinearities are present, the robust distances can be used to help visualize important regression models such as generalized linear models.