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Asymptotic Behaviors of Nearest Neighbor Kernel Density Estimator in Left-truncated Data | ||
Journal of Sciences, Islamic Republic of Iran | ||
مقاله 7، دوره 25، شماره 1، خرداد 2014، صفحه 57-67 اصل مقاله (755.24 K) | ||
نوع مقاله: Original Paper | ||
نویسنده | ||
V. Fakoor | ||
Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Islamic Republic of Iran | ||
چکیده | ||
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. | ||
کلیدواژهها | ||
Asymptotic normality؛ Left-truncation؛ Nearest neighbor؛ Strong consistency | ||
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