Nonparametric density estimation for nonnegative data, using symmetrical-based inverse and reciprocal inverse Gaussian kernels through dual transformation

DOI10.1016/j.jspi.2017.08.008zbMath1377.62108OpenAlexW2757814065MaRDI QIDQ1681054

Yoshihide Kakizawa

Publication date: 17 November 2017

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2017.08.008

zbMATH Keywords

density estimationdual transformationboundary-bias-free\(\log\)-symmetrical densityBirnbaum-Saunderssymmetrical-based inverse Gaussian density

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20) Characterization and structure theory of statistical distributions (62E10)

Related Items (12)

Limiting bias-reduced Amoroso kernel density estimators for non-negative data ⋮ Multivariate non-central Birnbaum-Saunders kernel density estimator for nonnegative data ⋮ Generalized Birnbaum–Saunders kernel for hazard rate function estimation ⋮ Generalised gamma kernel density estimation for nonnegative data and its bias reduction ⋮ Unnamed Item ⋮ Mellin-Meijer kernel density estimation on \(\mathbb{R}^+\) ⋮ A class of Birnbaum-Saunders type kernel density estimators for nonnegative data ⋮ Multiplicative bias correction for asymmetric kernel density estimators revisited ⋮ Asymptotic properties of Dirichlet kernel density estimators ⋮ Multivariate elliptical-based Birnbaum-Saunders kernel density estimation for nonnegative data ⋮ Higher-order bias corrections for kernel type density estimators on the unit or semi-infinite interval ⋮ Recursive asymmetric kernel density estimation for nonnegative data

Uses Software

• KernSmooth
• DLMF

Cites Work

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