Skewing methods for two-parameter locally parametric density estimation

Ming-Yen Cheng, Edwin Choi, Jianqing Fan, Peter Hall

Research output: Contribution to journalArticlepeer-review

Abstract

A 'skewing' method is shown to effectively reduce the order of bias of locally parametric estimators, and at the same time retain positivity properties. The technique involves first calculating the usual locally parametric approximation in the neighbourhood of a point x' that is a short distance from the place x where the we wish to estimate the density, and then evaluating this approximation at x. By way of comparison, the usual locally parametric approach takes x'=x. In our construction, x'-x depends in a very simple way on the bandwidth and the kernel, and not at all on the unknown density. Using skewing in this simple form reduces the order of bias from the square to the cube of bandwidth; and taking the average of two estimators computed in this way further reduces bias, to the fourth power of bandwidth. On the other hand, variance increases only by at most a moderate constant factor.
Original languageEnglish
Pages (from-to)169-182
Number of pages14
JournalBernoulli
Volume6
Issue number1
Publication statusPublished - Feb 2000

User-Defined Keywords

  • bias reduction
  • kernel methods
  • Local least squares
  • local likelihood
  • local linear methods
  • score function
  • weighted least squares

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