Hahn, PR;
Murray, JS;
Manolopoulou, I;
(2016)
A Bayesian partial identification approach to inferring the prevalence of accounting misconduct.
Journal of the American Statistical Association
, 111
(513)
pp. 14-26.
10.1080/01621459.2015.1084307.
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Abstract
This paper 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 is available - inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.
| Type: | Article |
|---|---|
| Title: | A Bayesian partial identification approach to inferring the prevalence of accounting misconduct |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1080/01621459.2015.1084307 |
| Publisher version: | http://dx.doi.org/10.1080/01621459.2015.1084307 |
| Language: | English |
| Additional information: | This is an Accepted Manuscript of an article published by Taylor & Francis in the Journal of the American Statistical Association on 5 May 2016, available online: http://www.tandfonline.com/10.1080/01621459.2015.1084307. |
| Keywords: | Bayesian inference, nonlinear regression, partial identification, sampling bias, sensitivity analysis, set identification |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1436784 |
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