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.