Adaptive wavelet estimation of a density from mixtures under multiplicative censoring

Yogendra P. Chaubey*, Christophe Chesneau, Hassan Doosti

*Corresponding author for this work

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

In this paper, a mixture model under multiplicative censoring is considered. We investigate the estimation of a component of the mixture (a density) from the observations. A new adaptive estimator based on wavelets and a hard thresholding rule is constructed for this problem. Under mild assumptions on the model, we study its asymptotic properties by determining an upper bound of the mean integrated squared error over a wide range of Besov balls. We prove that the obtained upper bound is sharp.

Original languageEnglish
Pages (from-to)638-659
Number of pages22
JournalStatistics
Volume49
Issue number3
DOIs
Publication statusPublished - 2015
Externally publishedYes

Keywords

  • density estimation
  • mixture
  • Besov balls
  • wavelets
  • hard thresholding

Fingerprint Dive into the research topics of 'Adaptive wavelet estimation of a density from mixtures under multiplicative censoring'. Together they form a unique fingerprint.

Cite this