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The Kernel density estimation of nonparametric model

Jifu Nong

Open publisher page 2 citations

Abstract

Four nonparametric estimates of a density function are investigated. Two model estimates are defined from a global kernel estimate, while the other two are defined from a global kernel estimate of the first derivative of the density function. We show that each of these model estimates attains the same rate of convergence as the usual sample model. Then, Monte-Carlo simulations illustrate on finite samples the utility of the method based on the local estimate of the first derivative.

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What this paper is about

Four nonparametric estimates of a density function are investigated. Two model estimates are defined from a global kernel estimate, while the other two are defined from a global kernel estimate of the first derivative of the density function. We show that each of these model estimates attains the same rate of convergence as the usual sample model. Then, Monte-Carlo simulations illustrate on finite samples the utility of the method based on the local estimate of the first derivative.

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Available abstract

Four nonparametric estimates of a density function are investigated. Two model estimates are defined from a global kernel estimate, while the other two are defined from a global kernel estimate of the first derivative of the density function. We show that each of these model estimates attains the same rate of convergence as the usual sample model. Then, Monte-Carlo simulations illustrate on finite samples the utility of the method based on the local estimate of the first derivative.

Key concepts: Multivariate kernel density estimation, Kernel density estimation, Kernel (algebra), Nonparametric statistics, Variable kernel density estimation, Computer science, Estimation, Density estimation

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