Selection of Predictors in Distance-Based Regression |
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Authors: | Eva Boj Del Val M Mercè Claramunt Bielsa Josep Fortiana |
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Institution: | 1. Department of Economical, Financial and Actuarial Mathematics , University of Barcelona , Spain evaboj@ub.edu;3. Department of Economical, Financial and Actuarial Mathematics , University of Barcelona , Spain |
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Abstract: | Distance-based regression is a prediction method consisting of two steps: from distances between observations we obtain latent variables which, in turn, are the regressors in an ordinary least squares linear model. Distances are computed from actually observed predictors by means of a suitable dissimilarity function. Being generally nonlinearly related with the response, their selection by the usual F tests is unavailable. In this article, we propose a solution to this predictor selection problem by defining generalized test statistics and adapting a nonparametric bootstrap method to estimate their p-values. We include a numerical example with automobile insurance data. |
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Keywords: | Automobile insurance data Distance-based regression Nonparame-tric bootstrap Predictors selection |
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