  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.
