Abstract Since 1992, the Innocence Projects (IP) have helped over 270 wrongly convicted persons prove their innocence and gain freedom. Despite the important successes of the IP's these exonerations may be a miniscule sample of the number of wrongly convicted persons who languish in prison. Statistical research on possible identifiers of likely 'exonerable' cases does not exist: current work is merely descriptive and non-inferential. We demonstrate an inferential methodology designed for the Innocence Projects: a multi-state hazard model with an augmentation via the parametric copula. Our approach is designed to exploit records that are readily available to exoneration workers. This approach offers a coherent, statistical framework for identification of significant and important factors on data that are readily available to the IP'
