title: Nonparametric Bayesian estimation of the three-way receiver operating characteristic surface.
abstract: We describe a nonparametric Bayesian approach for estimating the three-way ROC surface based on mixtures of finite Polya trees (MFPT) priors. Mixtures of finite Polya trees are robust models that can handle nonstandard features in the data. We address the difficulties in modeling continuous diagnostic data with skewness, multimodality, or other nonstandard features, and how parametric approaches can lead to misleading results in such cases. Robust, data-driven inference for the ROC surface and for the volume under the ROC surface is obtained. A simulation study is performed to assess the performance of the proposed method. Methods are applied to data from a magnetic resonance spectroscopy study on human immunodeficiency virus patients.
authors: Department of Statistics and Operations Research and Center of Statistics and Applications, Faculty of Sciences, Lisbon University, Building C6, 1749-016 Lisbon, Portugal. vanda.kinets@gmail.com
authors: Vanda
authors: V
authors: Inácio
authors: Antónia A
authors: AA
authors: Turkman
authors: Christos T
authors: CT
authors: Nakas
authors: Todd A
authors: TA
authors: Alonzo
mesh_terms: D001224:Aspartic Acid / Q000031:analogs & derivatives / Q000378:metabolism; D001499:Bayes Theorem; D001699:Biometry; D003404:Creatinine / Q000378:metabolism; D015658:HIV Infections / Q000150:complications; D006801:Humans; D009422:Nervous System Diseases / Q000150:complications / Q000175:diagnosis / Q000378:metabolism; D012372:ROC Curve; D018709:Statistics, Nonparametric
chemical_list: D001224:Aspartic Acid; C000179:N-acetylaspartate; D003404:Creatinine