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Jones, M.C., Marron, J.S. and Sheather, S.J. (1996) A Brief Survey of Bandwidth Selection for Density Estimation. Journal of the American Statistical Association, 91, 401-407.
https://doi.org/10.1080/01621459.1996.10476701

has been cited by the following article:

• TITLE: Kumaraswamy-Adaptive Normal Kernel Densities for Robust Smoothing of Skewed, Outlier-Contaminated Data

  AUTHORS: Kazeem A. Adepoju, Galin L. Jones

  KEYWORDS: Kernel Density Estimation, Density Smoothing, Integrated Squared Error, Skewed Distributions, Kernel Adaptation, Kumaraswamy Transformation, Monte-Carlo Kernel Comparison

  JOURNAL NAME: Open Journal of Statistics, Vol.16 No.2, April 14, 2026

  ABSTRACT: Kernel Density Estimation (KDE) is widely used for estimating unknown probability densities. Classical kernel forms are fixed-shape smoothers that may degrade under skewness and contamination. This study evaluates a Kumaraswamy-transformed Normal kernel (KwNormal) against standard kernels via Monte-Carlo replication and Integrated Squared Error (ISE). Results confirm the consistent dominance and stability of KwNormal across sample sizes.