Reducing bias in nonparametric density estimation via bandwidth dependent kernels: L1 view uri icon

abstract

  • We define a new bandwidth-dependent kernel density estimator that improves existing convergence rates for the bias, and preserves that of the variation, when the error is measured in L-1. No additional assumptions are imposed to the extant literature. (C) 2016 Elsevier B.V. All rights reserved.

publication date

  • 2017
  • 2017
  • 2017