Asymmetric Kernel Density Estimation for Biased Data

Yoshihide Kakizawa

doi:10.20944/preprints202306.1944.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 27 June 2023 / Approved: 28 June 2023 / Online: 28 June 2023 (04:36:50 CEST)

Nonparametric density estimation for nonnegative data is considered in a situation where a random sample is not directly available but the data are instead observed from the length-biased sampling. Due to the boundary bias problem of the location-scale kernel, the approach in this paper is an application of asymmetric kernel. Two nonparametric density estimators are proposed. The mean integrated squared error, strong consistency, and asymptotic normality of the estimators are investigated. Some simulations illustrate the finite sample performance of the estimators.

Biased data; nonparametric density estimation; boundary bias; asymmetric kernel

Computer Science and Mathematics, Probability and Statistics

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Asymmetric Kernel Density Estimation for Biased Data

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