On the J-test for nonnested hypotheses and Bayesian extension

Abstract Davidson and MacKinnon’s J-test was developed to test non-nested model specification. In empirical applications, however, when the alternate specifications fit the data well the J test may fail to distinguish between the true and false models: the J test will either reject, or fail to reject both specifications. In such cases we show that it is possible to use the information generated in the process of applying the J-test to implement a Bayesian approach that provides an unequivocal and acceptable solution. Jeffreys’ Bayes factors offer ways of obtaining the posterior probabilities of the competing models and relative ranking of the competing hypotheses. We further show that by using approximations of Schwarz Information Criterion and Bayesian Information Criterion we can use the classical estimates of the log of the maximum likelihood which are available from the estimation procedures used to implement the J test to obtain Bayesian posterior odds and posterior probabilities of the competing nested and non- nested specifications without having to specify prior distributions and going through the rigorous Bayesian computations.specification testing, non-nested hypotheses, Bayes factor, Bayesian Information Criteria, Marginal likelihood

3D printing for bio-synthetic biliary stents

Three-dimensional (3D) printing is an additive manufacturing method that holds great potential in a variety of future patient-specific medical technologies. This project validated a novel crosslinked polyvinyl alcohol (XL-PVA) 3D printed stent infused with collagen, human placental mesenchymal stem cells (PMSCs), and cholangiocytes. The biofabrication method in the present study examined 3D printing and collagen injection molding for rapid prototyping of customized living biliary stents with clinical applications in the setting of malignant and benign bile duct obstructions. XL-PVA stents showed hydrophilic swelling and addition of radiocontrast to the stent matrix improved radiographic opacity. Collagen loaded with PMSCs contracted tightly around hydrophilic stents and dense choloangiocyte coatings were verified through histology and fluorescence microscopy. It is anticipated that design elements used in these stents may enable appropriate stent placement, provide protection of the stent-stem cell matrix against bile constituents, and potentially limit biofilm development. Overall, this approach may allow physicians to create personalized bio-integrating stents for use in biliary procedures and lays a foundation for new patient-specific stent fabrication techniques

On the J-test for nonnested hypotheses and Bayesian extension

Abstract Davidson and MacKinnon’s J-test was developed to test non-nested model specification. In empirical applications, however, when the alternate specifications fit the data well the J test may fail to distinguish between the true and false models: the J test will either reject, or fail to reject both specifications. In such cases we show that it is possible to use the information generated in the process of applying the J-test to implement a Bayesian approach that provides an unequivocal and acceptable solution. Jeffreys’ Bayes factors offer ways of obtaining the posterior probabilities of the competing models and relative ranking of the competing hypotheses. We further show that by using approximations of Schwarz Information Criterion and Bayesian Information Criterion we can use the classical estimates of the log of the maximum likelihood which are available from the estimation procedures used to implement the J test to obtain Bayesian posterior odds and posterior probabilities of the competing nested and non- nested specifications without having to specify prior distributions and going through the rigorous Bayesian computations

On the J-test for nonnested hypotheses and Bayesian extension

Abstract Davidson and MacKinnon’s J-test was developed to test non-nested model specification. In empirical applications, however, when the alternate specifications fit the data well the J test may fail to distinguish between the true and false models: the J test will either reject, or fail to reject both specifications. In such cases we show that it is possible to use the information generated in the process of applying the J-test to implement a Bayesian approach that provides an unequivocal and acceptable solution. Jeffreys’ Bayes factors offer ways of obtaining the posterior probabilities of the competing models and relative ranking of the competing hypotheses. We further show that by using approximations of Schwarz Information Criterion and Bayesian Information Criterion we can use the classical estimates of the log of the maximum likelihood which are available from the estimation procedures used to implement the J test to obtain Bayesian posterior odds and posterior probabilities of the competing nested and non- nested specifications without having to specify prior distributions and going through the rigorous Bayesian computations