Nonparametric density estimation for symmetric distributions by contaminated data

被引：6

作者：

Maiboroda, Rostyslav ^{[1
]}

Sugakova, Olena ^{[1
]}

机构：

[1] Kyiv Natl Univ, Dept Probabil & Stat, Fac Mech & Math, Kiev, Ukraine

来源：

METRIKA | 2012年 / 75卷 / 01期

关键词：

Asymptotic normality; Finite mixture model; Symmetric distribution; Kernel density estimate; Rule of thumb; MISE; CONFIDENCE-INTERVALS; BOOTSTRAP; SELECTION;

D O I：

10.1007/s00184-010-0317-5

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 semiparametric two-component mixture model is considered, in which the distribution of one (primary) component is unknown and assumed symmetric. The distribution of the other component (admixture) is known. We consider three estimates for the pdf of primary component: a naive one, a symmetrized naive estimate and a symmetrized estimate with adaptive weights. Asymptotic behavior and small sample performance of the estimates are investigated. Some rules of thumb for bandwidth selection are discussed.

引用

页码：109 / 126

页数：18

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