Bayesian Nonparametric Approaches for ROC Curve Inference

Research output: Chapter in Book/Report/Conference proceedingOther chapter contribution

Abstract

The development of medical diagnostic tests is of great importance in clinical practice, public health, and medical research. The receiver operating characteristic (ROC) curve is a popular tool for evaluating the accuracy of such tests. We review Bayesian nonparametric methods based on Dirichlet process mixtures and the Bayesian bootstrap for ROC curve estimation and regression. The methods are illustrated by means of data concerning diagnosis of lung cancer in women.
Original languageEnglish
Title of host publicationNonparametric Bayesian Inference in Biostatistics
EditorsR. Mitra, P. Müller
PublisherSpringer-Verlag
Pages327-344
Number of pages18
ISBN (Print)978-3-319-19517-9 978-3-319-19518-6
DOIs
Publication statusPublished - 2015

Publication series

NameFrontiers in Probability and the Statistical Sciences
PublisherSpringer

Keywords

  • Biostatistics,Statistical Theory and Methods,Statistics for Life Sciences
  • Medicine
  • Health Sciences

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