Asymptotic bounds for the expected L1 error of a multivariate kernel density estimator

Lasse Holmström and Jussi Klemelä

Journal of Multivariate Analysis, 1992, vol. 42, issue 2, 245-266

Abstract: The kernel estimator of a multivariate probability density function is studied. An asymptotic upper bound for the expected L1 error of the estimator is derived. An asymptotic lower bound result and a formula for the exact asymptotic error are also given. The goodness of the smoothing parameter value derived by minimizing an explicit upper bound is examined in numerical simulations that consist of two different experiments. First, the L1 error is estimated using numerical integration and, second, the effect of the choice of the smoothing parameter in discrimination tasks is studied.

Keywords: nonparametric; density; estimation; multivariate; kernel; estimator; L1; error; discrimination; numerical; simulations (search for similar items in EconPapers)
Date: 1992
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Citations: View citations in EconPapers (4)

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