Consistent bandwidth selection for kernel binary regression

Naomi Altman, Brenda MacGibbon

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


The use of nonparametric regression techniques for binary regression is a promising alternative to parametric methods. As in other nonparametric smoothing problems, the choice of smoothing parameter is critical to the performance of the estimator and the appearance of the resulting estimate. In this paper, we discuss the use of selection criteria based on estimates of squared prediction risk and show consistency and asymptotic normality of the selected bandwidths. The usefulness of the methods is explored on a data set and in a small simulation study.

Original languageEnglish (US)
Pages (from-to)121-137
Number of pages17
JournalJournal of Statistical Planning and Inference
Issue number1
StatePublished - Jul 1 1998

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty
  • Applied Mathematics


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