The abstract of doctoral dissertation 'nonlinear wavelet density estimation and hazard rate estimation with data missing at random'

被引：0
|
作者：
Zou, Yuye [1 ,2 ,3 ,4 ]
Fan, Guoliang [1 ]
Zhang, Riquan [2 ]
机构：
[1] Shanghai Maritime Univ, Sch Econ & Management, Shanghai, Peoples R China
[2] East China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai, Peoples R China
[3] Shanghai Maritime Univ, Sch Econ & Management, Shanghai 201306, Peoples R China
[4] East China Normal Univ, Sch Stat, Key Lab Adv Theory & Applicat Stat & Data Sci MOE, Shanghai 200062, Peoples R China
来源：
STATISTICAL THEORY AND RELATED FIELDS | 2020年 / 4卷 / 01期
关键词：
Asymptotic normality; integral square error; mean integral square error; missing at random; non-linear wavelet;
D O I：
10.1080/24754269.2019.1653161
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 thesis, we establish non-linear wavelet density estimators and studying the asymptotic properties of the estimators with data missing at random when covariates are present. The outstanding advantage of non-linear wavelet method is estimating the unsoothed functions, however, the classical kernel estimation cannot do this work. At the same time, we study the larger sample properties of the ISE for hazard rate estimator.
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页码：117 / 119
页数：3
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