Abstract
This article describes the use of flexible Bayesian regression models for estimating a partially identified probability function. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors on the partially identified component of the regression model. The new methodology is illustrated on an important problem where only partially observed data are available—inferring the prevalence of accounting misconduct among publicly traded U.S. businesses. Supplementary materials for this article are available online.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 14-26 |
| Number of pages | 13 |
| Journal | Journal of the American Statistical Association |
| Volume | 111 |
| Issue number | 513 |
| DOIs | |
| State | Published - Jan 2 2016 |
| Externally published | Yes |
Keywords
- Bayesian inference
- Nonlinear regression
- Partial identification
- Sampling bias
- Sensitivity analysis
- Set identification
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty
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Cite this
Hahn, P., Murray, J. S., & Manolopoulou, I. (2016). A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct. Journal of the American Statistical Association, 111(513), 14-26. https://doi.org/10.1080/01621459.2015.1084307