Wavelet block thresholding for copula density estimation under biased sampling

被引：2

作者：

Shirazi, Esmaeil ^{[1
]}

Ghanbari, Bahareh ^{[2
,3
]}

Yarmohammadi, Masoud ^{[2
]}

机构：

[1] Gonbad Kavous Univ, Fac Sci, Dept Stat, Gonbad Kavous, Iran

[2] Payame Noor Univ, Dept Stat, Tehran, Iran

[3] Payame Noor Univ, Dept Stat, POB 193954697, Tehran, Iran

来源：

JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | 2023年 / 93卷 / 14期

关键词：

Biased data; copula; non-parametric estimation; wavelets; weighted distributions; NONPARAMETRIC-ESTIMATION; WEAK-CONVERGENCE; MODEL;

D O I：

10.1080/00949655.2023.2193894

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

In this paper, we consider the non-parametric estimation of the copula density under biased data. The contributions are both theoretical and practical. In the first part, we propose and develop a new wavelet-based methodology for this problem. In particular, a BlockShrink estimator is constructed, and we prove that it enjoys powerful mean integrated squared error properties over Besov balls. The second part is devoted to the applied aspect: we compare the performance of the wavelet-based estimator with that of a recently introduced kernel-based estimator through a detailed simulation study.

引用

页码：2512 / 2533

页数：22

共 37 条

[1] Thresholding methods to estimate copula density [J].