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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data
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نویسنده
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Zamini R. ,Fakoor V. ,Sarmad M.
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منبع
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journal of sciences islamic republic of iran - 2014 - دوره : 25 - شماره : 1 - صفحه:57 -67
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چکیده
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Kernel density estimators are the basic tools for density estimation in non-parametric statistics. the k-nearest neighbor kernel estimators represent a special form of kernel density estimators, in which the bandwidth is varied depending on the location of the sample points. in this paper, we initially introduce the k-nearest neighbor kernel density estimator in the random left-truncation model, and then prove some of its asymptotic behaviors, such as strong uniform consistency and asymptotic normality. in particular, we show that the proposed estimator has truncation-free variance. simulations are presented to illustrate the results and show how the estimator behaves for finite samples. moreover, the proposed estimator is used to estimate the density function of a real data set.
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کلیدواژه
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Asymptotic normality ,Left-truncation ,Nearest neighbor
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آدرس
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ferdowsi university of mashhad, ایران, ferdowsi university of mashhad, ایران, ferdowsi university of mashhad, ایران
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Authors
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