Wavelet-based quantile density function estimation under random censorship

Esmaeil Shirazi, Hassan Doosti*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review


In this paper, the estimation of a quantile density function in the presence of right censored data is investigated. A new wavelet-based methodology for the estimation of the quantile function will is proposed. In particular, an adaptive hard thresholding wavelet estimator is constructed. Under mild assumptions on the model, we prove that it enjoys powerful mean integrated squared error properties over Besov balls. While existing estimators of the quantile density function are not good at the tails, our proposed estimators perform well at the tails. The comparison of the proposed estimator has been made with estimators given by Jones (1992) Ann Inst Stat Math 44(4):721–727 and Soni et al. (2012) Comput Stat Data Anal 56(12):3876–3886 graphically and in terms of the mean integrated square error (MISE) for the uncensored case.
Original languageEnglish
Title of host publicationStatistics for data science and policy analysis
EditorsAzizur Rahman
Place of PublicationSingapore
PublisherSpringer, Springer Nature
Number of pages10
ISBN (Electronic)9789811517358
ISBN (Print)9789811517341
Publication statusPublished - 2020
EventApplied Statistics and Policy Analysis Conference 2019 - Wagga Wagga, Australia
Duration: 5 Sep 20196 Sep 2019


ConferenceApplied Statistics and Policy Analysis Conference 2019
Abbreviated titleASPAC2019
CityWagga Wagga


  • Adaptivity
  • Quantile density function
  • L² risk function
  • Wavelets
  • Hard thresholding


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