Linear wavelet-based estimation for derivative of a density under random censorship

Yogendra P. Chaubey, Hassan Doosti, Esmaeel Shirazi, B. L. S. Prakasa Rao

Research output: Contribution to journalArticlepeer-review

Abstract

In this paper we consider estimation of the derivative of a density based on wavelets methods using randomly right censored data. We extend the results regarding the asymptotic convergence rates due to Prakasa Rao (1996) and Chaubey et al. (2008) under random censorship model. Our treatment is facilitated by results of Stute (1995) and Li (2003) that enable us in demonstrating that the same convergence rates are achieved as in Prakasa Rao (1996) and Chaubey et al. (2008).

Original languageEnglish
Pages (from-to)41-51
Number of pages11
JournalJournal of the Iranian Statistical Society
Volume9
Issue number1
Publication statusPublished - Mar 2010
Externally publishedYes

Keywords

  • Besove space
  • censored data
  • nonparametric estimation of derivative of a density
  • wavelets

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