Linear Wavelet-Based Estimation for Derivative of a Density under Random Censorship

被引：0
|
作者：
Chaubey, YocTendra P. [1 ]
Doosti, Hassan [2 ]
Shirazi, Esmaeel [3 ]
Rao, B. L. S. Prakasa [4 ]
机构：
[1] Concordia Univ, Dept Math & Stat, Montreal, PQ, Canada
[2] Islamic Azad Univ, Fac Sci, Dept Stat, Mashhad Branch, Mashhad, Iran
[3] Ferdowsi Univ Mashhad, Dept Stat, Mashhad, Iran
[4] Univ Hyderabad, Dept Math & Stat, Hyderabad, Andhra Pradesh, India
来源：
JIRSS-JOURNAL OF THE IRANIAN STATISTICAL SOCIETY | 2010年 / 9卷 / 01期
关键词：
Besove space; censored data; nonparametric estimation of derivative of a density; wavelets;
D O I：
暂无
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
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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页码：41 / 51
页数：11
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