Nonparametric direct density ratio estimation using beta kernel

被引：3
|
作者：
Igarashi, Gaku [1 ]
机构：
[1] Univ Tsukuba, Fac Engn Informat & Syst, Dept Policy & Planning Sci, 1-1-1 Tennodai, Tsukuba, Ibaraki 3058573, Japan
来源：
STATISTICS | 2020年 / 54卷 / 02期
基金：
日本学术振兴会;
关键词：
Nonparametric density ratio estimator; boundary bias problem; beta kernel;
D O I：
10.1080/02331888.2020.1722671
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：
A new nonparametric density ratio estimator using the beta kernel is proposed. It is shown that the beta kernel density ratio estimator (KDRE) is free of boundary or tail bias, and the asymptotic properties of the beta KDRE are derived. Simulation studies are conducted to illustrate the finite sample performance of the beta KDRE.
引用
收藏
页码：257 / 280
页数：24
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