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The generalized sigmoidal quantile function

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

In this note we introduce a new smooth nonparametric quantile function estimator based on a newly defined generalized expectile function and termed the sigmoidal quantile function estimator. We also introduce a hybrid quantile function estimator, which combines the optimal properties of the classic kernel quantile function estimator with our new generalized sigmoidal quantile function estimator. The generalized sigmoidal quantile function can estimate quantiles beyond the range of the data, which is important for certain applications given smaller sample sizes. This property of extrapolation is illustrated in order to improve standard bootstrap smoothing resampling methods.

Original languageEnglish
Pages (from-to)799-813
Number of pages15
JournalCommunications in Statistics Part B: Simulation and Computation
Volume53
Issue number2
DOIs
StatePublished - 2024

Keywords

  • Bootstrap
  • Expectiles
  • Hermitian quantile function
  • Kernel quantile estimator
  • Tail extrapolation

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