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摘要翻译：
通过仿真研究，我们比较了超模反卷积问题中反卷积核密度估计器的有限样本性能与两个渐近正态性定理所预测的渐近性能。我们的结果表明，对于较低的噪声水平和中等的样本容量，渐近理论和估计器的有限样本性能之间的匹配是不令人满意的。另一方面，我们表明这两种方法在较高的噪声水平下产生了相当接近的结果。这些观察结果反过来又为研究反卷积问题提供了额外的动机，假设误差项方差$\sigma^2\\-0$为样本量$n\\\\infty.$
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英文标题：
《Some thoughts on the asymptotics of the deconvolution kernel density
estimator》
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作者：
Bert van Es and Shota Gugushvili
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最新提交年份：
2008
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分类信息：
一级分类：Statistics 统计学
二级分类：Methodology 方法论
分类描述：Design, Surveys, Model Selection, Multiple Testing, Multivariate Methods, Signal and Image Processing, Time Series, Smoothing, Spatial Statistics, Survival Analysis, Nonparametric and Semiparametric Methods
设计，调查，模型选择，多重检验，多元方法，信号和图像处理，时间序列，平滑，空间统计，生存分析，非参数和半参数方法
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英文摘要：
Via a simulation study we compare the finite sample performance of the deconvolution kernel density estimator in the supersmooth deconvolution problem to its asymptotic behaviour predicted by two asymptotic normality theorems. Our results indicate that for lower noise levels and moderate sample sizes the match between the asymptotic theory and the finite sample performance of the estimator is not satisfactory. On the other hand we show that the two approaches produce reasonably close results for higher noise levels. These observations in turn provide additional motivation for the study of deconvolution problems under the assumption that the error term variance $\sigma^2\to 0$ as the sample size $n\to\infty.$
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PDF链接：
https://arxiv.org/pdf/801.26
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