Authors : Saad Abed Madhi and Saad Ali Sultan
Abstract: The goal of this study is to study maximum likelihood estimates for a bivariate distribution with missing data using an algebraic geometry tool, namely, Grobner basis techniques. In maximum likelihood estimation, the parameters of the model are estimated by maximizing the likelihood function which maps the parameters to the likelihood of observing the given data. By transforming this optimization problem into a polynomial optimization problem, it can be shown that the solutions of the likelihood equations can be computed using Grobner basis technique.
Saad Abed Madhi and Saad Ali Sultan, 2019. Grobner Basis for Bivariate Normal with Missing Data Model Estimation Problem. Journal of Engineering and Applied Sciences, 14: 10137-10142.