DOI

This paper provides a set of uniform consistency results with rates for nonparametric density and regression estimators smoothed by the beta kernel having support on the unit interval. Weak and strong uniform convergence is explored on the basis of expanding compact sets and general sequences of smoothing parameters. The results in this paper are useful for asymptotic analysis of two-step semiparametric estimation using a first-step kernel estimate as a plug-in. We provide simulations and a real data example illustrating attractive properties of the estimators.

Original languageEnglish
Pages (from-to)1353-1382
Number of pages30
JournalScandinavian Journal of Statistics
Volume49
Issue number3
Early online date10 Jan 2022
DOIs
StatePublished - Sep 2022

    Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

    Research areas

  • beta kernel, boundary bias, nonparametric density estimation, nonparametric regression estimation, rates of convergence, MULTIPLICATIVE BIAS CORRECTION, DENSITY-ESTIMATION, REGRESSION, MODELS

ID: 94060566