Eva Boj del Val M. Mercedes Claramunt Bielsa Jose Fortiana Gregori (Universitat de Barcelona)
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 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.
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Paper provided by Universitat de Barcelona. Espai de Recerca en Economia in its series Working Papers in Economics with number
154.
Length: 22 pages Date of creation: 2006 Date of revision: Handle: RePEc:bar:bedcje:2006154
Contact details of provider: Postal: Espai de Recerca en Economia, Facultat de Ciències Econòmiques. Tinent Coronel Valenzuela, Num 1-11 08034 Barcelona. Spain. Web page: http://www.ere.ub.es More information through EDIRC
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Find related papers by JEL classification: C80 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - General C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Hypothesis Testing G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Semiparametric and Nonparametric Methods
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References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
Flachaire, E., 1999.
"A Better Way to Bootstrap Pairs,"
Papers
9924, Catholique de Louvain - Center for Operations Research and Economics.
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