Posterior probability intervals in Bayesian wavelet estimation

We use saddlepoint approximation to derive credible intervals for Bayesian wavelet regression estimates. Simulations show that the resulting intervals perform better than the best existing metho

Unifying Practical Uncertainty Representations: II. Clouds

There exist many simple tools for jointly capturing variability and incomplete information by means of uncertainty representations. Among them are random sets, possibility distributions, probability intervals, and the more recent Ferson's p-boxes and Neumaier's clouds, both defined by pairs of possibility distributions. In the companion paper, we have extensively studied a generalized form of p-box and situated it with respect to other models . This paper focuses on the links between clouds and other representations. Generalized p-boxes are shown to be clouds with comonotonic distributions. In general, clouds cannot always be represented by random sets, in fact not even by 2-monotone (convex) capacities.Comment: 30 pages, 7 figures, Pre-print of journal paper to be published in International Journal of Approximate Reasoning (with expanded section concerning clouds and probability intervals

A UNIFIED APPROACH TO SENSITIVITY ANALYSIS IN EQUILIBRIUM DISPLACEMENT MODELS: COMMENT

It is pointed out that the Chebychev confidence intervals and maximum p-values advocated by Davis and Espinoza for sensitivity analysis of equilibrium displacement models are unnecessary. Desired probability intervals and probabilities can be accurately estimated without resorting to gross approximations.simulation, probability distributions, empirical quantiles, Research Methods/ Statistical Methods,

Credit Shocks and Cycles: a Bayesian Calibration Approach

This paper asks how well a general equilibrium agency cost model describes the dynamic relationship between credit variables and the business cycle. A Bayesian VAR is used to obtain probability intervals for empirical correlations. The agency cost model is found to predict the leading, countercyclical correlation of spreads with output when shocks arising from the credit market contribute to output fluctuations. The contribution of technology shocks is held at conventional RBC levels. Sensitivity analysis shows that moderate prior calibration uncertainty leads to significant dispersion in predictedcorrelations. Most predictive uncertainty arises from a single parameter.agency costs, credit cycles, calibration, shocks.

Propagating imprecise probabilities through event trees

Event trees are a graphical model of a set of possible situations and the possible paths going through them, from the initial situation to the terminal situations. With each situation, there is associated a local uncertainty model that represents beliefs about the next situation. The uncertainty models can be classical, precise probabilities; they can also be of a more general, imprecise probabilistic type, in which case they can be seen as sets of classical probabilities (yielding probability intervals). To work with such event trees, we must combine these local uncertainty models. We show this can be done efficiently by back-propagation through the tree, both for precise and imprecise probabilistic models, and we illustrate this using an imprecise probabilistic counterpart of the classical Markov chain. This allows us to perform a robustness analysis for Markov chains very efficiently