Past Government Shutdowns: Key Resources

[Excerpt] When federal government agencies and programs lack budget authority, they experience a “funding gap.” Under the Antideficiency Act (31 U.S.C. § 1341 et seq.), they must cease operations, except in certain circumstances. When there is a funding gap that affects many federal entities, the situation is often referred to as a government shutdown. In the past, there have occasionally been government shutdowns, the longest of which lasted 21 days, from December 16, 1995, to January 6, 1996. This report provides an annotated list of historical documents and other resources related to several past government shutdowns. The report also includes links to full-text documents when available. There is limited information and guidance related to shutdowns, and it is difficult to predict what might happen in the event of one, but information about past events may help inform future deliberations. This report will be updated as additional resources are identified

Bayesian learning of joint distributions of objects

There is increasing interest in broad application areas in defining flexible joint models for data having a variety of measurement scales, while also allowing data of complex types, such as functions, images and documents. We consider a general framework for nonparametric Bayes joint modeling through mixture models that incorporate dependence across data types through a joint mixing measure. The mixing measure is assigned a novel infinite tensor factorization (ITF) prior that allows flexible dependence in cluster allocation across data types. The ITF prior is formulated as a tensor product of stick-breaking processes. Focusing on a convenient special case corresponding to a Parafac factorization, we provide basic theory justifying the flexibility of the proposed prior and resulting asymptotic properties. Focusing on ITF mixtures of product kernels, we develop a new Gibbs sampling algorithm for routine implementation relying on slice sampling. The methods are compared with alternative joint mixture models based on Dirichlet processes and related approaches through simulations and real data applications.Comment: Appearing in Proceedings of the 16th International Conference on Artificial Intelligence and Statistics (AISTATS) 2013, Scottsdale, AZ, US

A Bayesian partial identification approach to inferring the prevalence of accounting misconduct

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