A new non parametric estimator for Pdf based on inverse gamma distribution
A new non parametric estimator for Pdf based on inverse gamma distribution
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一种新的基于逆伽玛分布的Pdf非参数估计器
DOI：
10.1080/03610926.2014.972575
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Fathi
中科院分区：
文献类型：
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作者：
A. M. Mousa;M. Kh. Hassan;A. Fathi
摘要考虑非参数方法估计（0,∞）上支持的概率密度函数（Pdf）。这种方法是逆核函数。我们证明了它具有与gamma，倒数逆高斯和逆高斯核相同的性质，因此它没有边界偏差，非负，并且它达到了平均积分平方误差的最佳收敛速度。并给出了估计量的偏差和方差等性质。比较了Pdf反核估计的带宽选择方法。
ABSTRACT The non parametric approach is considered to estimate probability density function (Pdf) which is supported on(0, ∞). This approach is the inverse gamma kernel. We show that it has same properties as gamma, reciprocal inverse Gaussian, and inverse Gaussian kernels such that it is free of the boundary bias, non negative, and it achieves the optimal rate of convergence for the mean integrated squared error. Also some properties of the estimator were established such as bias and variance. Comparison of the bandwidth selection methods for inverse gamma kernel estimation of Pdf is done.
用于密度估计的内核数据压缩
DOI：
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发表时间：
2006
期刊：
影响因子：
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作者：
Atsuyuki;Kogure;Masahiko;Sagae
通讯作者：
Sagae