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Multivariate elliptical-based Birnbaum-Saunders kernel density estimation for nonnegative data. (English) Zbl 1480.62099

Summary: 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.

MSC:

62H12 Estimation in multivariate analysis
62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference

Software:

KernSmooth
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Full Text: DOI

References:

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