Nonparametric regression estimates with censored data based on block thresholding method

E. Shirazi*, H. Doosti, H. A. Niroumand, N. Hosseinioun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

Here we consider wavelet-based identification and estimation of a censored nonparametric regression model via block thresholding methods and investigate their asymptotic convergence rates. We show that these estimators, based on block thresholding of empirical wavelet coefficients, achieve optimal convergence rates over a large range of Besov function classes, and in particular enjoy those rates without the extraneous logarithmic penalties that are usually suffered by term-by-term thresholding methods. This work is extension of results in Li et al. (2008). The performance of proposed estimator is investigated by a numerical study.

Original languageEnglish
Pages (from-to)1150-1165
Number of pages16
JournalJournal of Statistical Planning and Inference
Volume143
Issue number7
DOIs
Publication statusPublished - Jul 2013
Externally publishedYes

Keywords

  • Block thresholding
  • Censored data
  • Minimax estimation
  • Nonparametric regression
  • Rate of convergence

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