Nonparametric density estimation for symmetric distributions by contaminated data

Nonparametric density estimation for symmetric distributions by contaminated data
复制标题

污染数据对称分布的非参数密度估计

DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
O. Sugakova
O. Sugakova
中科院分区：
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文献类型：
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作者：
R. Maiboroda;O. Sugakova

文献摘要

被引文献

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考虑了一个半参数两组分混合模型，其中一个（主）组分的分布是未知的，并假设对称。已知其他组分（混合物）的分布。我们考虑了主分量的概率密度函数的三种估计：朴素估计、对称化朴素估计和自适应权的对称化估计。研究了估计的渐近性态和小样本性能。讨论了带宽选择的一些经验法则。
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.