Flexible prior specification for partially identified nonlinear regression with binary responses
Year: 2014
Type: preprint
Abstract: . This paper adapts tree-based Bayesian regression models for estimating a par-tially identified probability function. In doing so, ideas from the recent literature on Bayesian partial identification are applied within a sophisticated applied regression context. Our ap-proach permits efficient sensitivity analysis concerning the posterior impact of priors over the partially identified component of the regression model. The new methodology is illus-trated on an important problem where we only have partially observed data – inferring the prevalence of accounting misconduct among publicly traded U.S. businesses.
Source: arXiv (Cornell University)
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1514368038