Thursday, March 22, 2018 - 3:00pm
209 W Eighteenth Ave (EA), Room 170
Fully Bayesian Estimation Under Informative Sampling
Terrance Savitsky, Bureau of Labor Statistics
Bayesian estimation is increasingly popular for performing model based inference to support policymaking. These data are often collected from surveys under informative sampling designs where subject inclusion probabilities are designed to be correlated with the response variable of interest. Sampling weights constructed from marginal inclusion probabilities are typically used to form an exponentiated pseudo likelihood that adjusts the population likelihood for estimation on the sample. We propose an alternative adjustment based on a Bayes rule construction that simultaneously performs weight smoothing and estimates the population model parameters in a fully Bayesian construction. We formulate conditions on known marginal and pairwise inclusion probabilities that define a class of sampling designs where L1 consistency of the joint posterior is guaranteed. We compare performances between the two approaches on synthetic data. We demonstrate our method on an application from the National Health and Nutrition Examination Survey exploring the relation-
ship between caffeine consumption and systolic blood pressure.
Note: Seminars are free and open to the public. Reception to follow.