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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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    1. Christmann, Andreas & Rousseeuw, Peter J., 2001. "Measuring overlap in binary regression," Computational Statistics & Data Analysis, Elsevier, vol. 37(1), pages 65-75, July.
    2. Escabias, M. & Aguilera, A.M. & Valderrama, M.J., 2007. "Functional PLS logit regression model," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4891-4902, June.
    3. 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.
    4. Gao, Sujuan & Shen, Jianzhao, 2007. "Asymptotic properties of a double penalized maximum likelihood estimator in logistic regression," Statistics & Probability Letters, Elsevier, vol. 77(9), pages 925-930, May.
    5. Aguilera, Ana M. & Escabias, Manuel & Valderrama, Mariano J., 2008. "Discussion of different logistic models with functional data. Application to Systemic Lupus Erythematosus," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 151-163, September.
    6. Gareth M. James, 2002. "Generalized linear models with functional predictors," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 411-432, August.
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