Hunting for significance: Bayesian classifiers under a mixture loss function

DOI10.1016/j.jspi.2014.02.010zbMath1306.62140OpenAlexW2164636521MaRDI QIDQ460651

Igar Fuki, Linda Zhao, X. Han, Lawrence D. Brown

Publication date: 13 October 2014

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://repository.upenn.edu/statistics_papers/576

zbMATH Keywords

nonparametric estimationBayes classification rulefalse discoveriesfalse nondiscoverieshigh-dimensional sparse inference

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric estimation (62G05) Bayesian problems; characterization of Bayes procedures (62C10)

Uses Software

• KernSmooth
• GSA

Cites Work

• On testing the significance of sets of genes
• Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
• An exploration of aspects of Bayesian multiple testing
• Nonparametric empirical Bayes and compound decision approaches to estimation of a high-dimensional vector of normal means
• Size, power and false discovery rates
• Calibration and empirical Bayes variable selection
• Oracle and Adaptive Compound Decision Rules for False Discovery Rate Control
• Multiple Hypotheses Testing with Weights
• Empirical Bayes Estimates for Large-Scale Prediction Problems
• Admissible Estimators, Recurrent Diffusions, and Insoluble Boundary Value Problems
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