Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis

By Michael A. Newton. 2008

722 citations recorded by the source corpus.

Subfields: Control and Systems Engineering

View Paper: https://www.semanticscholar.org/paper/898639280e5a418911d4ba47be552f89dd67296b

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In Control and Systems Engineering

Citations in the corpus

Order Selection in Finite Mixture Models With a Nonsmooth Penalty (2008, methodology): “Efron (2004) provided an empirical Bayes approach for estimating an appropriate null hypothesis when many hypotheses are tested simultaneously.”
Testing the Number of Components in Normal Mixture Regression Models (2015, background): “Thereby, one needs to determine the number of components of the finite mixture model that describes the data (Efron, 2004; McLachlan et al., 2004, 2006).”
Optimal rates of convergence for estimating the null density and proportion of nonnull effects in large-scale multiple testing (2010, methodology): “Our estimator of the proportion performs well uniformly in all the cases in comparison to the estimators proposed in Storey (2002) and Efron (2004).”
SIMULTANEOUS INFERENCE : WHEN SHOULD HYPOTHESIS TESTING PROBLEMS BE COMBINED? (2008, methodology): “Obtained using R program locfdr, Efron (2007b), MLE fitting method”
Hypothesis test for normal mixture models: The EM approach (2009, methodology): “Normal mixture models are also used to account for heterogeneity in the age of onset for male and female schizophrenia patients [Everitt (1996)], and used in hematology studies [McLaren (1996)]. They play a fundamental role in cluster analysis [Tadesse, Sha and Vannucci (2005) and Raftery and Dean (”
Asymptotic Properties of Bayes Risk of a General Class of Shrinkage Priors in Multiple Hypothesis Testing Under Sparsity (2013, methodology): “Some early examples of one-group shrinkage priors are the t-prior (Tipping (2001)), the Laplace prior in the context of Bayesian Lasso (Park and Casella (2008) and Hans (2009)) and the family of normal–exponential–gamma priors (Griffin and Brown (2005)).”
Estimating high-dimensional intervention effects from observational data (2008, methodology): “In order to decide which causal scores should be considered “signiﬁcantly high,” we use the local false discovery rate (FDR) [7].”
Covariate‐assisted ranking and screening for large‐scale two‐sample inference (2019, background): “A partial solution to resolve the issue is to estimate the empirical null distributions (Efron, 2004; Jin and Cai, 2007) for different groups, instead of using the theoretical null directly.”
Doubly Robust Internal Benchmarking and False Discovery Rates for Detecting Racial Bias in Police Stops (2009, methodology): “Following Efron (2004), we estimate f0(z) with the empirical null assumed to be normal with parameters estimated using the location and curvature of the central part of the histogram, resulting in an N(0.1, s ¼ 1.4) density.”
Proportion of non‐zero normal means: universal oracle equivalences and uniformly consistent estimators (2008, methodology): “In this example, based on 15 patients diagnosed with breast cancer (7 with the BRCA1 mutation and 8 with the BRCA2 mutation), microarray data were generated for the same set of 3226 genes.”
On testing the significance of sets of genes (2006, positive): “Figure 4 concerns the “BRCA data,” Efron (2004), a microarray experiment comparing two classes of breast cancer patients, N = 3226 genes, n = 15 microarrays, n = 7 for class 1, n2 = 8 for class 2.”
Significance Analysis of Spectral Count Data in Label-free Shotgun Proteomics*S (2008, methodology): “The rate of false positives in the selection of differentially expressed proteins based on Bayes factors can be estimated using a mixture model-based method of local FDR control (33, 34).”