Bootstrapping pairs in Distance-Based Regression
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 in general nonlinearly related with the response their selection by the usual F tests is unavailable. In this paper we propose a solution to this predictor selection problem, by defining generalized test statistics and adapting a non-parametric bootstrap method to estimate their p-values. We include a numerical example with automobile insurance data.
|Date of creation:||2006|
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- Flachaire, Emmanuel, 1999.
"A better way to bootstrap pairs,"
Elsevier, vol. 64(3), pages 257-262, September.
- Emmanuel Flachaire, 1999. "A better way to bootstrap pairs," Post-Print halshs-00175892, HAL.
- FLACHAIRE, Emmanuel, 1999. "A better way to bootstrap pairs," CORE Discussion Papers 1999024, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- J. Gower & P. Legendre, 1986. "Metric and Euclidean properties of dissimilarity coefficients," Journal of Classification, Springer;The Classification Society, vol. 3(1), pages 5-48, March. Full references (including those not matched with items on IDEAS)
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