TY  - JOUR
T1  - A Bayesian Partial Identification Approach to Inferring the Prevalence of Accounting Misconduct
JF  - Journal of the American Statistical Association
Y1  - 2016
A1  - P. R. Hahn
A1  - J. S. Murray
A1  - I. Manolopoulou
AB  - 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.
VL  - 111
UR  - http://www.tandfonline.com/doi/full/10.1080/01621459.2015.1084307
IS  - 513
ER  - 

TY  - RPRT
T1  - Flexible prior specification for partially identified nonlinear regression with binary responses
Y1  - 2014
A1  - P. R. Hahn
A1  - J. S. Murray
A1  - I. Manolopoulou
AB  - This paper adapts tree-based Bayesian regression models for estimating a partially identified probability function. In doing so, ideas from the recent literature on Bayesian partial identification are applied within a sophisticated applied regression context. Our approach permits efficient sensitivity analysis concerning the posterior impact of priors over the partially identified component of the regression model. The new methodology is illustrated on an important problem where we only have partially observed data -- inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.
PB  - arXiv
UR  - https://arxiv.org/abs/1407.8430v1
IS  - 1407.8430
ER  -