publication . Other literature type . Article . 1980

On Improving Convergence Rates for Nonnegative Kernel Density Estimators

David W. Scott; George R. Terrell;
Open Access English
  • Published: 01 Sep 1980
  • Publisher: The Institute of Mathematical Statistics
Abstract
To improve the rate of decrease of integrated mean square error for nonparametric kernel density estimators beyond $0(n^{-\frac{4}{5}}),$ we must relax the constraint that the density estimate be a bonafide density function, that is, be nonnegative and integrate to one. All current methods for kernel (and orthogonal series) estimators relax the nonnegativity constraint. In this paper we show how to achieve similar improvement by relaxing the integral constraint only. This is important in applications involving hazard function and likelihood ratios where negative density estimates are awkward to handle.
Subjects
free text keywords: Nonparametric density estimation, kernel estimation, rates of convergence, 62G05, Statistics, Probability and Uncertainty, Statistics and Probability, Kernel density estimation, Kernel (statistics), Estimator, Convergence (routing), Density estimation, Kernel regression, Mathematics, Multivariate kernel density estimation, Variable kernel density estimation, Statistics
Any information missing or wrong?Report an Issue