Many statistical and econometric learning methods rely on Bayesian ideas, often applied or reinterpreted in a frequentist setting. Two leading examples are shrinkage estimators and model averaging estimators, such as weighted-average least squares (WALS). In many instances, the accuracy of these...

We propose a nonparametric Bayesian approach for conducting inference on probabilistic surveys. We use this approach to study whether U.S. Survey of Professional Forecasters density projections for output growth and inflation are consistent with the noisy rational expectations hypothesis. We...

Non-homogeneous post-processing is often used to improve the predictive performance of probabilistic ensemble forecasts. A common quantity to develop, test, and demonstrate new methods is the near-surface air temperature frequently assumed to follow a Gaussian response distribution. However,...

We summarize the statistical properties of statistics computed from independent random bitstreams including the commonly discussed support and cosine similarity. We derive the moments of the asymptotically normal approximation to the sampling distribution of the cosine similarity of independent...

Holst (1985) introduced a discrete spacings model that is related to the Bose-Einstein distribution and obtained the distribution of the number of vacant slots in an associated circle covering problem. We correct his expression for its probability mass function, obtain the first two moments, and...

In Bayesian nonparametric inference, random discrete probability measures are commonly used as priors within hierarchical mixture models for density estimation and for inference on the clustering of the data. Recently it has been shown that they can also be exploited in species sampling...