James G. Scott

Also known as

statistical methodology for high-dimensional data sets, with applications in a diverse set of areas spanning the social, physical, and biomedical sciences. Three areas of methodological focus include (1) large-scale multiple testing, anomaly-detection and screening problems, where the rate of false discoveries must be controlled in order to yield viable inferences; (2) inference in sparse models; and (3) the application of data-augmentation theory and algorithms to improve the efficiency of Bayesian inference in large-scale models for discrete data sets.-faculty profile

Links

• University of Texas at Austin faculty profile
• Github
• CV
• Additional VIAF (19164536265332020632)

Identifiers

• Open LibraryOL15417198A
• WikidataQ121931094
• VIAF1174155919207439730006