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

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  • Kakizawa, Yoshihide

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.

Suggested Citation

  • Kakizawa, Yoshihide, 2022. "Multivariate elliptical-based Birnbaum–Saunders kernel density estimation for nonnegative data," Journal of Multivariate Analysis, Elsevier, vol. 187(C).
    • Handle: RePEc:eee:jmvana:v:187:y:2022:i:c:s0047259x21001123
    DOI: 10.1016/j.jmva.2021.104834
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    References listed on IDEAS

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