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ABSTRACT
For this project, Non-parametric density estimation was our area of concentration due to it being the most frequently used. Univariate case was only considered though it can be applied to the multivariate case. The kernel density estimation as it is the most used technology was being used for this project, with this method developing rapidly and vastly. In this analysis, three specific kernels (Gaussian, Epanechnikov, and Biweight) were utilized to assign weights to nearby data points based on their distance from the point of interest. The resulting graphs for each kernel vividly demonstrated the distinct shapes and rates of weight assignment, providing valuable insights into their individual characteristics.
