On the M-Estimator under Third Moment Condition - Rundong Luo; Yiming Chen; Shuai Song (Mathematics, 2022)
Estimating the expected value of a random variable by data-driven methods is one of the most fundamental problems in statistics. In this study, we present an extension of Olivier Catoni’s classical M-estimators of the empirical mean, which focus on the heavy-tailed data by imposing more precise inequalities on exponential moments of Catoni’s estimator. We show that our works behave better than Catoni‘s both in practice and theory. The performances are illustrated in the simulation and real data.