Parameter Estimation Optimization for the Sarmanov Distribution: A Comparative Analysis of Strategies

Abstract

This study focuses on the optimization of parameter estimation for the Sarmanov distribution, aiming to enhance the fit of the modeling with real data. A comparative analysis of different strategies is conducted to identify the most effective approaches. The research explores the use of partial derivatives associated with those parameters that are directly linked to the dependency structure and statistical techniques to estimate these parameters with a higher precision. By evaluating and comparing the results obtained from these strategies, valuable insights are gathered regarding their performance and applicability. The findings contribute to the optimization of the parameter estimation methods for the Sarmanov distribution, leading to improve its modeling and opening new fields of investigation.