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Using copula information in wavelet estimation of bivariate density function based on censorship observations

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  • Bahareh Ghanbari
  • Esmaeil Shirazi

Abstract

This article discusses the nonparametric estimation of a bivariate density function using copula information under right censoring. We propose an adaptive estimator based on wavelet methods and the formulae for the asymptotic mean integrated squared error(MISE) is used to get the near optimal rate on a large functional class of regular densities. In particular, the asymptotic formulae for MISE in the context of kernel density estimators is derived in the case of censoring. Finally, the consistency of the proposed estimators is established and its effectiveness is validated through a numerical simulations.

Suggested Citation

  • Bahareh Ghanbari & Esmaeil Shirazi, 2024. "Using copula information in wavelet estimation of bivariate density function based on censorship observations," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(5), pages 1810-1824, March.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:5:p:1810-1824
    DOI: 10.1080/03610926.2022.2113798
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