Linear Wavelet-Based Estimation for Derivative of a Density under Random Censorship
نویسندگان
چکیده مقاله:
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).
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عنوان ژورنال
دوره 9 شماره None
صفحات 41- 51
تاریخ انتشار 2010-03
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