Title: Bayesian Dependent Data Modeling for Official Statistics and Survey Methodology
Presenters: Jonathan R. Bradley, Florida State University and Scott H. Holan, University of Missouri
Date and Time: Friday, January 21, 12:00 p.m. – 2:00 p.m. Eastern Time
Sponsor: Survey Research Methods Section
Registration Deadline: Thursday, January 20, at 12:00 p.m. Eastern time
Model-based statistics have experienced tremendous growth in official statistics and survey methodology due to their utility across a wide range of applications. Recently, various hierarchical Bayesian dependent data models have been proposed. These models take advantage of different dependencies that are inherent in the data, often arising because of the way the data are collected. That is, there are often spatial, spatio-temporal, cross-spatial resolution, and/or multivariate relationships that arise. This presentation reviews basic Bayesian hierarchical modeling strategies and highlights some recent advances made in this area. Importantly, we consider both unit-level and area-level models and in both the Gaussian and non-Gaussian settings. Finally, we illustrate the various methods through several applications and detail the computational challenges that arise.
SRMS Members: $20
ASA Members: $30
Student ASA Member: $25
Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers.
Registered persons will be sent an email the afternoon of Thursday, January 20, with the information to join the webinar and, if possible, a link to download and print a copy of the presentation slides.