Bayesian Sparse Propensity Score Estimation for Unit Nonresponse

Date
2018-07-31
Authors
Sang, Hejian
Kim, Jae Kwang
Goh, Gyuhyeong
Kim, Jae Kwang
Journal Title
Journal ISSN
Volume Title
Publisher
Source URI
Altmetrics
Authors
Research Projects
Organizational Units
Statistics
Organizational Unit
Journal Issue
Series
Abstract

Nonresponse weighting adjustment using propensity score is a popular method for handling unit nonresponse. However, including all available auxiliary variables into the propensity model can lead to inefficient and inconsistent estimation, especially with high-dimensional covariates. In this paper, a new Bayesian method using the Spike-and-Slab prior is proposed for sparse propensity score estimation. The proposed method is not based on any model assumption on the outcome variable and is computationally efficient. Instead of doing model selec- tion and parameter estimation separately as in many frequentist methods, the proposed method simultaneously selects the sparse response probability model and provides consistent parameter estimation. Some asymptotic properties of the proposed method are presented. The efficiency of this sparse propensity score estimator is further improved by incorporating related auxiliary variables from the full sample. The finite-sample performance of the proposed method is investigated in two limited simulation studies, including a partially simulated real data example from the Korean Labor and Income Panel Survey.

Description
<p>This is a pre-print made available through arxiv: <a href="https://arxiv.org/abs/1807.10873" target="_blank">https://arxiv.org/abs/1807.10873</a>.</p>
Keywords
Approximate Bayesian computation, Data augmentation, High dimensional data, Missing at random, Spike-and-Slab prior
Citation
Collections