Novel Estimator

Research on novel estimators focuses on developing improved methods for estimating various statistical quantities, addressing limitations of existing techniques. Current efforts concentrate on enhancing the accuracy and efficiency of estimators for mutual information, conditional entropy (particularly in regression problems), and parameters within specific models like the Rasch model and linear contextual bandits. These advancements are crucial for improving the reliability of machine learning algorithms, enabling more accurate predictability analysis in regression, and facilitating robust inference in various applications, including A/B testing and high-dimensional data analysis.