Multivariate wavelet-based density estimation with size-biased data

被引：9

作者：

Shirazi, Esmaeil ^{[1
]}

Doosti, Hassan ^{[2
]}

机构：

[1] Gonbad Kavous Univ, Fac Sci, Dept Stat, Gonbad Kavous 4971799151, Iran

[2] Mashhad Univ Med Sci, Dept Biostat & Epidemiol, Mashhad, Iran

来源：

STATISTICAL METHODOLOGY | 2015年 / 27卷

关键词：

Besov spaces; Density estimation; Size-biased data; Wavelet; KERNEL;

D O I：

10.1016/j.stamet.2015.05.002

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 employ wavelet method to propose a multivariate density estimator based on a biased sample. We investigate the asymptotic rate of convergence of the proposed estimator over a large class of densities in the Besov space, B-pq(s). Moreover, we prove the consistency of our estimator when the expectation of weight function is unknown. This paper is an extension of results in Ramirez and Vidakovic (2010) and Chesneau et al. (2012) to the multivariate case. (C) 2015 Elsevier B.V. All rights reserved.

引用

页码：12 / 19

页数：8

共 25 条

[1] ON MULTIVARIATE KERNEL ESTIMATION FOR SAMPLES FROM WEIGHTED DISTRIBUTIONS [J].