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论文类型：会议论文

发表时间：2010-06-06

收录刊物：EI、CPCI-S、Scopus

卷号：6063

期号：PART 1

页面范围：465-472

关键字：nonparametric kernel regression; probability density estimation; cumulative distribution functions

摘要：In this research, a local kernel regression method was proposed to improve the computational efficiency after analyzing the kernel weights oh the non-parametric kernel regression. Based on the correlation between the distribution function and the probability density function, together with the nonparametric local kernel regression we developed a new probability density estimation method. With the proper setting of the sparse factor, the number of the kernels involved in the kernel smooth was controlled, and the density was estimated with highly fitness and smoothness. According to the simulations, we can see that the proposed method shows a very well performance both in the accuracy and the efficiency.