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Point separation in logistic regression on Hilbert space-valued variables

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  • Kazakevičiūtė, Agne
  • Olivo, Malini

Abstract

We study point separation for the logistic regression model for Hilbert space-valued variables. We prove that the separating hyperplane can be found from a finite set of candidates and give an upper bound for the probability of point separation.

Suggested Citation

  • Kazakevičiūtė, Agne & Olivo, Malini, 2017. "Point separation in logistic regression on Hilbert space-valued variables," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 84-88.
    • Handle: RePEc:eee:stapro:v:128:y:2017:i:c:p:84-88
    DOI: 10.1016/j.spl.2017.04.019
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    References listed on IDEAS

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    5. M. Aguilera-Morillo & Ana Aguilera & Manuel Escabias & Mariano Valderrama, 2013. "Penalized spline approaches for functional logit regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(2), pages 251-277, June.
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