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Variable selection in infinite-dimensional problems

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  • Aneiros, Germán
  • Vieu, Philippe

Abstract

This paper is on regression models when the explanatory variable is a function. The question is to look for which among the pn discretized values of the function must be incorporated in the model. The aim of the paper is to show how the continuous structure of the data allows to develop new specific variable selection procedures, which improve the rates of convergence of the estimated parameters and need much less restrictive assumptions on pn.

Suggested Citation

  • Aneiros, Germán & Vieu, Philippe, 2014. "Variable selection in infinite-dimensional problems," Statistics & Probability Letters, Elsevier, vol. 94(C), pages 12-20.
    • Handle: RePEc:eee:stapro:v:94:y:2014:i:c:p:12-20
    DOI: 10.1016/j.spl.2014.06.025
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    References listed on IDEAS

    as
    1. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    2. Ming Yuan & Yi Lin, 2006. "Model selection and estimation in regression with grouped variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 49-67, February.
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