THE USE OF CONFIDENCE OR FIDUCIAL LIMITS ILLUSTRATED IN THE CASE OF THE BINOMIAL

By C. J. Clopper, E. S. Pearson. 1934

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Citations in the corpus

Consistent Accelerated Inference via Confident Adaptive Transformers (2021, methodology): “This result is based on Clopper-Pearson confidence interval for Binomial random variables [5].”
Auditing Differentially Private Machine Learning: How Private is Private SGD? (2020, methodology): “However, this approach incurs statistical uncertainty, which we correct for by using Clopper Pearson conﬁdence intervals [CP34].”
Certified Adversarial Robustness via Randomized Smoothing (2019, methodology): “Our main result is that smoothed classifier g is robust around x within the radius R = σ2 (Φ −1(pA)−Φ−1(pB)), where Φ−1 is the inverse of the standard Gaussian CDF.”
Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks (2014, methodology): “We calculate confidence intervals assuming a binomial distribution using the method by Clopper and Pearson (1934).”
Small Sample Confidence Intervals for the Odds Ratio (2004, methodology): “We will call this the Clopper–Pearson method (Clopper and Pearson, 1934).”
OptimalCutpoints: An R Package for Selecting Optimal Cutpoints in Diagnostic Tests (2014, methodology): “Moreover, the exact conﬁdence interval (Clopper and Pearson 1934) is being calculated by default for the sensitivity, speciﬁcity and predictive values measures (in this case the inference for these values is correct, since it is a cross-sectional study; if it were a case-control study, however, the…”
The Saw-Toothed Behavior of Power Versus Sample Size and Software Solutions (2002, methodology): “Clopper–Pearson method and dates back to their original paper, Clopper and Pearson (1934). Several applications to computing exact statistical intervals can be found in Hahn and Meeker (1991).”
Estimation of aquifer scale proportion using equal area grids: Assessment of regional scale groundwater quality (2010, methodology): “[21] The exact method [Clopper and Pearson, 1934] is a commonly recommended alternative to the Wald interval.”
A more powerful test for comparing two Poisson means (2004, unknown): “In particular, Chapman (1952) proposed a con8dence interval for the ratio � 1=� 2 which is deduced fromthe exact con8dence interval for the binom ial success probability due to Clopper and Pearsons (1934) .”
Nivolumab for Metastatic Renal Cell Carcinoma: Results of a Randomized Phase II Trial (2014, methodology): “For each treatment group, ORR was estimated along with exact 80% CI using the Clopper-Pearson method.(25) The dose-response relationship was evaluated using a two-sided 20%-level Cochran-Armitage test.”
Gender-preserving Debiasing for Pre-trained Word Embeddings (2019, methodology): “In particular, in both SemBias and SemBias-subset , GP (GN-GloVe) statistically signiﬁcantly outperforms GloVe and Hard-Glove according to Clopper-Pearson conﬁdence intervals (Clopper and Pearson, 1934).”
PAC Confidence Predictions for Deep Neural Network Classifiers (2020, background): “In particular, the Clopper-Pearson interval guarantees (4) (Clopper & Pearson, 1934; Brown et al., 2001), where s = b ∈ (cid:126)b b is the number of observed successes, n = | (cid:126)b | is the number of observations, and S is a Binomial random variable S ∼ Binomial ( n, θ ) .”
Rucaparib in relapsed, platinum-sensitive high-grade ovarian carcinoma (ARIEL2 Part 1): an international, multicentre, open-label, phase 2 trial. (2017, background): “women with relapsed, platinum-sensitive, high-grade ovarian carcinoma harbouring a germline BRCA mutation, with an objective response rate (ORR) of 66·7%.(24) To our knowledge, ARIEL2”