On kernel density estimation near endpoints

Shunpu Zhang, Rohana J. Karunamuni

Research output: Contribution to journalArticlepeer-review

57 Scopus citations


In this paper, we consider the estimation problem of f(0), the value of density f at the left endpoint 0. Nonparametric estimation of f(0) is rather formidable due to boundary effects that occur in nonparametric curve estimation. It is well known that the usual kernel density estimates require modifications when estimating the density near endpoints of the support. Here we investigate the local polynomial smoothing technique as a possible alternative method for the problem. It is observed that our density estimator also possesses desirable properties such as automatic adaptability for boundary effects near endpoints. We also obtain an 'optimal kernel' in order to estimate the density at endpoints as a solution of a variational problem. Two bandwidth variation schemes are discussed and investigated in a Monte Carlo study.

Original languageEnglish (US)
Pages (from-to)301-316
Number of pages16
JournalJournal of Statistical Planning and Inference
Issue number2
StatePublished - Jul 15 1998


  • Bandwidth variation
  • Boundary effects
  • Density estimation
  • Local polynomial smoothers
  • Optimal kernel
  • Primary 62G07
  • Secondary 62C20, 62J20

ASJC Scopus subject areas

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


Dive into the research topics of 'On kernel density estimation near endpoints'. Together they form a unique fingerprint.

Cite this