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Kumaraswamy-Adaptive Normal Kernel Densities for Robust Smoothing of Skewed, Outlier-Contaminated Data
School of Statistics, University of Minnesota, Minneapolis, USA
School of Statistics, University of Minnesota, Minneapolis, USA
- 1 School of Statistics, University of Minnesota, Minneapolis, USA
- 2 School of Statistics, University of Minnesota, Minneapolis, USA
Open Journal of Statistics·Volume 16 (2026)·Pages 107–120·Published 8 April 2026·DOI10.4236/ojs.2026.162006
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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.
KeywordsKernel Density EstimationDensity SmoothingIntegrated Squared ErrorSkewed DistributionsKernel AdaptationKumaraswamy TransformationMonte-Carlo Kernel Comparison
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