Title: Meta estimation of normal mean parameter: Seven perspectives of data integration
Authors: Peter Song - University of Michigan (United States) [presenting]
Abstract: Data integration has recently drawn considerable attention in the statistical literature. We will present a synergic treatment on the estimation of mean parameter of a normal distribution from seven different schools of statistics, which sheds light on the future development of data integration analytics. They include best linear unbiased estimation (BLUE), maximum likelihood estimation (MLE), Bayesian estimation, empirical Bayesian estimation (EBE), Fisher's fiducial estimation, generalized methods of moments (GMM) estimation, and empirical likelihood estimation (ELE). Their properties of scalability and distributed inference will be discussed and compared analytically and numerically.