Authors

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

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).

Keywords

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