Wavelet estimations of the derivatives of variance function in heteroscedastic model

被引：1
|
作者：
Kou, Junke [1 ]
Zhang, Hao [1 ]
机构：
[1] Guilin Univ Elect Technol, Sch Math & Computat Sci, Guilin 541004, Guangxi, Peoples R China
来源：
AIMS MATHEMATICS | 2023年 / 8卷 / 06期
基金：
中国国家自然科学基金;
关键词：
nonparametric wavelet estimation; derivative function; heteroscedastic model; Lp? risk; NONPARAMETRIC REGRESSION; CONDITIONAL VARIANCE; DENSITY-ESTIMATION;
D O I：
10.3934/math.2023734
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper studies nonparametric estimations of the derivatives r(m)(x) of the variance function in a heteroscedastic model. Using a wavelet method, a linear estimator and an adaptive nonlinear estimator are constructed. The convergence rates under Lp similar to(1 < p similar to < oo) risk of those two wavelet estimators are considered with some mild assumptions. A simulation study is presented to validate the performances of the wavelet estimators.
引用
收藏
页码：14340 / 14361
页数：22
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