Estimation Strategies with Nonignorable Non-Probability Survey Samples

Thomas M. Menino Convention & Exhibition Center 

We first provide an overview on inferential frameworks for analyzing non-probability survey samples. We then present some recent results on estimating participation probabilities (i.e., propensity scores) under ignorable or nonignorable participation mechanisms. In particular, we show that the pseudo maximum likelihood method of Chen, Li and Wu (2020, JASA), which was developed under the ignorability assumption, can be used to build a method for dealing with nonignorable participation mechanisms when there is a moderate or strong correlation between the study variable and auxiliary variables. Some empirical results from simulation studies will be presented.

Inverse probability weighting

Doubly robust estimation

Nonignorable participation

Pseudo maximum likelihood method

Instrumental variable