In this talk I will offer some perspectives on Bayesian inference for finite population quantities when the units in the population are assumed to exhibit complex dependencies. Beginning with an overview of Bayesian hierarchical models, including some that yield design-based Horvitz-Thompson estimators, the talk proceeds to introduce dependence in finite populations and sets out inferential frameworks for ignorable and nonignorable responses. Multivariate dependencies using graphical models and spatial processes are discussed and some salient features of two recent analyses for spatially oriented finite populations are presented.
1 Marzo 2024, ore 18
Sudipto Banerjee
Department of Biostatistics
Department of Statistics and Data Science
University of California, Los Angeles
Online: https://uniroma1.zoom.us/j/86881977368?pwd=S WRFcVFjMDZTa0lXZk05TE1zNm5adz09
Passcode: 432940