Multivariate elliptical-based Birnbaum–Saunders kernel density estimation for nonnegative data

Yoshihide Kakizawa

Journal of Multivariate Analysis, 2022, vol. 187, issue C

Abstract: The Birnbaum–Saunders distribution has been generalized in various ways, for parametric or nonparametric statistical inference. In this paper, as a remedy for the boundary bias problem of nonparametric density estimation, a family of deformed multivariate elliptical-based non-central Birnbaum–Saunders kernel density estimators is introduced, and its asymptotic mean integrated squared error is discussed. The simulation results reveal that a novel log-elliptical density estimator has a good performance in small sample size.

Keywords: Boundary bias problem; Elliptical-based Birnbaum–Saunders distribution; Log-elliptical distribution; Multivariate density estimation (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1016/j.jmva.2021.104834

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