Representative Points from a Mixture of Two Normal Distributions

In recent years, the mixture of two-component normal distributions (MixN) has attracted considerable interest due to its flexibility in capturing a variety density shapes. this paper, we investigate problem discretizing MixN by fixed number points under minimum mean squared error (MSE-RPs). Motivated Fang-He algorithm, provide an effective computational procedure with high precision for generating numerical approximations MSE-RPs from MixN. We have explored properties nonlinear system used generate and demonstrated convergence procedure. studies, proposed computation is compared k-means algorithm. From application perspective, potential advantages statistical inference.Our studies show that can significantly improve Kernel estimation.