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Multivariate Density Estimation Using a Multivariate Weighted Log-Normal Kernel

Gaku Igarashi ()
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Gaku Igarashi: University of Tsukuba

Sankhya A: The Indian Journal of Statistics, 2018, vol. 80, issue 2, 247-266

Abstract: Abstract This paper suggests a multivariate asymmetric kernel density estimation using a multivariate weighted log-normal (LN) kernel for non-negative multivariate data. Asymptotic properties of the multivariate weighted LN kernel density estimator are studied. Simulation studies are also conducted in the bivariate situation.

Keywords: Nonparametric density estimation; Boundary problem; Asymmetric kernel; Multivariate log-normal density; Primary 62G07; Secondary 62G20 (search for similar items in EconPapers)
Date: 2018
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Handle: RePEc:spr:sankha:v:80:y:2018:i:2:d:10.1007_s13171-018-0125-y