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Implementing PLS for distance-based regression: computational issues

Author

Listed:
  • Eva Boj
  • Aurea Grané
  • Josep Fortiana
  • M. Claramunt

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.
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Suggested Citation

  • Eva Boj & Aurea Grané & Josep Fortiana & M. Claramunt, 2007. "Implementing PLS for distance-based regression: computational issues," Computational Statistics, Springer, vol. 22(2), pages 237-248, July.
    • Handle: RePEc:spr:compst:v:22:y:2007:i:2:p:237-248
    DOI: 10.1007/s00180-007-0035-2
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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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