Nonparametric density estimation for multivariate bounded data using two non-negative multiplicative bias correction methods

Benedikt Funke and Rafael Kawka

Computational Statistics & Data Analysis, 2015, vol. 92, issue C, 148-162

Abstract: Two new multiplicative bias correction techniques for nonparametric multivariate density estimation in the context of positively supported data are proposed. Both methods reach an optimal rate of convergence of the mean squared error of order O(n−8/(8+d)), where d is the dimension of the underlying data set. In addition, they overcome the boundary effect and their values are always non-negative. Asymptotic properties like bias and variance are investigated. Moreover, the performance of both estimators is studied in Monte Carlo simulations and in two real data examples.

Keywords: Asymmetric kernels; Bias correction; Multivariate density estimation (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (11)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:92:y:2015:i:c:p:148-162

DOI: 10.1016/j.csda.2015.07.006

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