The researchers developed high-order series expansions using specific mathematical techniques to analyze probability density functions and cumulative distribution functions. These expansions are based on Hermite and generalized Laguerre orthogonal polynomials, allowing for different parameter values in the weighting functions. The key findings show that these expansions can accurately represent the densities and distributions of various data sets, even when the parameters in the weighting functions do not match perfectly.