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There are various methods to estimate the parameters in the binormal model for the ROC curve. In this paper, we propose a conceptually simple and computationally feasible Bayesian estimation method using a rank-based likelihood. Posterior consistency is also established. We compare the new method with other estimation methods and conclude that our estimator(More)
Density estimation, especially multivariate density estimation, is a fundamental problem in nonparametric inference. Dirichlet mixture priors are often used in practice for such problem. However, asymptotic properties of such priors have only been studied in the univariate case. We extend L 1-consistency of Dirichlet mixutures in the multivariate density(More)
Receiver operating characteristic (ROC) curve is widely applied in measuring discriminatory ability of diagnostic or prognostic tests. This makes the ROC analysis one of the most active research areas in medical statistics. Many parametric and semiparametric estimation methods have been proposed for estimating the ROC curve and its functionals. In this(More)
This article proposes a Bayesian infinite mixture model for the estimation of the conditional density of an ergodic time series. A nonparametric prior on the conditional density is described through the Dirichlet process. In the mixture model, a kernel is used leading to a dynamic nonlinear autoregressive model. This model can approximate any linear(More)
SUMMARY We show that rate-adaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the kernel's covariance matrix parameter. We derive sufficient conditions on the prior specification that guarantee convergence to a true density at a rate that is minimax(More)
Variable selection techniques for the classical linear regression model have been widely investigated. Variable selection in fully nonparametric and additive regression models have been studied more recently. A Bayesian approach for nonparametric additive regression models is considered, where the functions in the additive model are expanded in a B-spline(More)
The talk describes a nonparametric Bayesian approach to estimating the regression function for binary response data measured with multiple covariates. A multiparameter Gaussian process, after some transformation, is used as a prior on the regression function. Such a prior does not require any assumption such as monotonicity or additivity of the covariate(More)
We propose a Dirichlet process mixture model (DPMM) for the P-value distribution in a multiple testing problem. The DPMM allows us to obtain posterior estimates of quantities such as the proportion of true null hypothesis and the probability of rejection of a single hypothesis. We describe a Markov chain Monte Carlo algorithm for computing the posterior and(More)
X-ray images of distant stars and galaxies are typically registered by low photon counts at the pixel level, for which the Poisson distribution is a sensible model description. The resulting count data can be represented in a multi-scale framework, where the likelihood function factorizes in functions of relative intensity parameters corresponding to(More)