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We propose a Bayesian nonparametric model to estimate rating migration matrices and default probabilities using the reinforced urn processes (RUP) introduced in Muliere et al. (2000). The estimated default probability becomes our prior information in a parametric model for the prediction of the...
Persistent link: https://www.econbiz.de/10011077595
The ROC curve is one of the most common statistical tools useful to assess classifier performance. The selection of the best classifier when ROC curves intersect is quite challenging. A novel approach for model comparisons when ROC curves show intersections is proposed. In particular, the...
Persistent link: https://www.econbiz.de/10010871438
We introduce a stochastic process based on nonhomogeneous Poisson processes and urn processes which can be reinforced to produce a mixture of semi-Markov processes. By working with the notion of exchangeable blocks within the process, we present a Bayesian nonparametric framework for handling...
Persistent link: https://www.econbiz.de/10008875776
In this paper we estimate income distributions, Lorenz curves and the related Gini index using a Bayesian nonparametric approach based on Polya tree priors. In particular, we propose an alternative approach for dealing with contaminated observations and extreme income values: avoiding the common...
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This paper introduces a bivariate Dirichlet process for modelling a partially exchangeable sequence of observables. The proposed model would be relevant when two distributions are unknown but are thought to be close to each other. For two random distributions with the same marginals, the belief...
Persistent link: https://www.econbiz.de/10005259243
Based on reinforced urn process introduced by Muliere et al. [2000. Urn schemes and reinforced random walks. Stochastic Process. Appl. 88(1), 59-78] we propose a Bayesian nonparametric approach to analyse a design determining the maximum tolerated dose (MTD) in Phase I clinical trials for new...
Persistent link: https://www.econbiz.de/10005259341