The Convergence Rate with Bootstrap for Multidimensional Density Functional Kernel Estimation

Abstract:

Bootstrap method is a statistical method proposed by the American Stanford University professor of Statistics Efron, which belongs to the parameters of statistical methods. According to a given sub-sample, we do not need its distributional assumptions or increase the sample information which can be described the overall distribution characteristics of statistical inference. The basic idea of the Bootstrap statistics is unknown and can not repeat the sampling distribution function instead of using a repeat sampling of the distribution function estimates. The independent identically distributed random variable series ,have the common probability density function, with .In the paper, combining with multidimensional density function, we discuss the convergence rate with Bootstrap method for the kernel estimation of the density functional .

• Periodical: Advanced Materials Research (Volumes 591-593)
• Pages: 2559-2563
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.591-593.2559
• Online since: November 2012
• Authors: De Wang Li
• Keywords: Bootstrap, Convergence Rate, Density Functional, Kernel Estimation

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