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Implementing PLS for distance-based regression: computational issues

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  • Boj, Eva
  • Grané, Aurea
  • Fortiana, Josep
  • Claramunt, M. Merce

Abstract

Distance-based regression allows for a neat implementation of the Partial Least Squares recurrence. In this paper we address practical issues arising when dealing with moderately large datasets (n ~ 10^4) such as those typical of automobile insurance premium calculations.

Suggested Citation

  • Boj, Eva & Grané, Aurea & Fortiana, Josep & Claramunt, M. Merce, 2006. "Implementing PLS for distance-based regression: computational issues," DES - Working Papers. Statistics and Econometrics. WS ws063514, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:ws063514
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    Cited by:

    1. Pedro Delicado, 2007. "Functional k-sample problem when data are density functions," Computational Statistics, Springer, vol. 22(3), pages 391-410, September.
    2. Eva Boj & Pedro Delicado & Josep Fortiana & Anna Esteve & Adria Caballe, 2012. "Local Distance-Based Generalized Linear Models using the dbstats package for R," Working Papers XREAP2012-11, Xarxa de Referència en Economia Aplicada (XREAP), revised May 2012.
    3. Eva Boj & Adrià Caballé & Pedro Delicado & Anna Esteve & Josep Fortiana, 2016. "Global and local distance-based generalized linear models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(1), pages 170-195, March.

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