False Discovery Rate–Adjusted Multiple Confidence Intervals for Selected Parameters

By Yoav Benjamini, D. Yekutieli. 2005

746 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4d999d8f888d51e1a7bc33f9b37a1146a066227d

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In Management Science and Operations Research

In Statistics and Probability

Citations in the corpus

Exact post-selection inference with the lasso (2013, methodology): “If we repeat the procedure for each j ∈ Ê, our intervals in fact control the false coverage rate (FCR) of Benjamini and Yekutieli (2005).”
Discovering the false discovery rate (2010, background): “In Benjamini and Yekutieli (2005) we demonstrated that confidence intervals that are constructed for selected parameters only, where selection depends on the observed values, cannot ensure nominal coverage even on average.”
Exact post-selection inference, with application to the lasso (2013, methodology): “If we repeat the procedure for each j ∈ Ê, our intervals in fact control the false coverage rate (FCR) of Benjamini and Yekutieli (2005).”
Multiple hypothesis testing in genomics (2014, background): “It is helpful to see the problem of multiple testing as a problem caused by selection [7, 8].”
High-dimensional inference in misspecified linear models (2015, background): “Another philosophy for inference in the high-dimensional setting is based on selective inference [3, 20, 27], but we do not consider this here.”
The Extent and Consequences of P-Hacking in Science (2015, background): “However, others note that many of the problems with publication bias reoccur with other approaches, such as reporting effect sizes and their confidence intervals [31] or Bayesian credible intervals [32].”
Multiple Testing for Exploratory Research (2011, methodology): “Methods have been formulated for selective inference (Benjamini and Yekutieli, 2005), but these still do not allow the full flexibility of exploratory selection.”
False discovery rates: a new deal (2016, background): “However, despite ongoing interest in this area from both frequentist (Benjamini and Yekutieli, 2005) and Bayesian (Zhao and Gene Hwang, 2012; Gelman and others, 2012) perspectives, in practice large-scale studies that assess many effects almost invariably focus on testing significance and controllin”
SLOPE—ADAPTIVE VARIABLE SELECTION VIA CONVEX OPTIMIZATION (2014, background): “, Benjamini and Yekutieli (2005); Wasserman and Roeder (2009); Meinshausen et al.”
Optimal Inference After Model Selection (2014, background): “By far the most similar to ours is the proposal of Benjamini and Yekutieli (2005) to control the FCR, who focus as we do on problems of selective inference.”
Large-scale inference (2010, background): “22) goes far back into the empirical Bayes literature, and played important roles in the profound investigations of multidimensional normal estimation of Brown (1971) and Stein (1981). Robbins (1956) includes the formula for E{δ|z} in (11.”