Explosive growth in computing power has made Bayesian methods for infinite-dimensional models - Bayesian nonparametrics - a nearly universal framework for inference, finding practical use in numerous subject areas. Written by leading researchers, this authoritative text draws on theoretical...

Recent models for credit risk management make use of Hidden Markov Models (HMMs). The HMMs are used to forecast quantiles of corporate default rates. Little research has been done on the quality of such forecasts if the underlying HMM is potentially mis-specified. In this paper, we focus on...

Summary We consider estimating a probability density p based on a random sample from this density by a Bayesian approach. The prior is constructed in two steps, by first constructing priors on a collection of models each expressing a qualitative prior guess on the true density, and next...

Abstract We give a precise statement and proof of a projection formula due to J. Robins for computing influence functions in models where the observation is a random process that is stopped by a coarsening-at-random mechanism. This projection formula is important for constructing estimating...

We construct an estimator of , based on a random sample of size n from a density f on the unit cube in . This estimator achieves the minimax rate for f known to belong to a multiple of the unit ball in a Hölder space of order [alpha], where [alpha]=d/4. We are mostly interested in the case that...

Let be the class of functions which are [alpha] times differentiable with derivatives bounded by numbers Mj on each given set Ij in a partition of . We obtain upper and lower bounds on the Lr(P)-bracketing entropy of .

We describe a novel approach to nonparametric point and interval estimation of a treatment effect in the presence of many continuous confounders. We show that the problem can be reduced to that of point and interval estimation of the expected conditional covariance between treatment and response...

Künstliche neuronale Netze sind ein in der Theorie oft beschriebenes und in der Praxis der Wirtschaftswissenschaften, Informatik sowie Natur- und Ingenieurwissenschaften häufig eingesetztes Instrument zur Durchführung von Datenanalysen. Darstellungen neuronaler Netze entstammen jedoch...