New technique reduces errors in data analysis, improving accuracy significantly.
Higher-order kernel estimation and kernel density derivative estimation are methods to improve accuracy in nonparametric kernel density estimation. This study compared these techniques using the Gaussian kernel estimator, a widely used method. Results showed that kernel density derivative estimation performed better than higher order kernel estimation in reducing errors, based on real-life data analysis.