Individual Comparisons by Ranking Methods

By F. Wilcoxon. 1945

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Citations in the corpus

Visual Prompt Tuning (2022, methodology): “We conduct non-parametric paired one-tailed t-test (the Wilcoxon signed-rank test [75]) on whether VPT-deep’s performance is greater than other fine-tuning methods across 19 VTAB tasks (the null hypothesis H0 states that the mean VTAB performance difference between VPT-deep and alternate baseline me”
Achieving Human Parity on Automatic Chinese to English News Translation (2018, methodology): “Results with statistically insignificant differences are grouped into clusters (according to Wilcoxon rank sum test [44] at p-level p ≤ 0.”
Continuous Doubly Constrained Batch Reinforcement Learning (2021, methodology): “For each method: we perform a head-to-head comparison against CDC across the D4RL tasks, reporting the p-value of a (one-sided) Wilcoxon signed rank test [61] that compares this method’s return against that of CDC (over the 32 tasks).”
NaturalSpeech: End-to-End Text-to-Speech Synthesis With Human-Level Quality (2022, methodology): “We further conduct Wilcoxon signed rank test [35] to measure whether the two systems are signiﬁcantly different or not in terms of CMOS evaluation.”
Revisiting Classifier Two-Sample Tests (2016, methodology): “ave nurtured a wide variety of two-sample tests. Classical two-sample tests include the t-test (Student, 1908), which tests for the difference in means of two samples; the Wilcoxon-Mann-Whitney test (Wilcoxon, 1945; Mann & Whitney, 1947), which tests for the difference in rank means of two sampl”
Investigating BERT’s Knowledge of Language: Five Analysis Methods with NPIs (2019, methodology): “A Wilcoxon signed-rank test (Wilcoxon, 1945) shows that within each environment and for each NPI, the acceptable sentences are more often rated as acceptable by our MTurk valida-tors than the unacceptable sentences (all p -values 0.001).”
Robust kernel density estimation (2008, methodology): “each case, an appropriate performance measure is adopted. These are explained in detail in Section 6.2. To compare a pair of methods across multiple data sets, we adopt the Wilcoxon signed-rank test (Wilcoxon, 1945). Given a performance measure, and given a pair of methods and , we compute the diere”
The Hitchhiker’s Guide to Testing Statistical Significance in Natural Language Processing (2018, methodology): “Wilcoxon signed-rank test (Wilcoxon, 1945) Like the sign test variants, this test is used when comparing two matched samples (e.”
Forecasting Macroeconomic Variables Using Neural Network Models and Three Automated Model Selection Techniques (2016, methodology): “Another robust way of considering the results is to use Wilcoxon’s signedrank test (Wilcoxon (1945)) for comparing forecasts from the DGP with the others.”
Let a biogeography-based optimizer train your Multi-Layer Perceptron (2014, methodology): “In order to judge whether the results of BBO differ from PSO, GA, ACO, ES, and PBIL in a statistically significant way, a nonparametric statistical test, Wilcoxon’s rank-sum test [76], was carried out at 5% significance level.”
Balancing Convergence and Diversity in Decomposition-Based Many-Objective Optimizers (2016, methodology): “test [63] at a 5% significance level is conducted on the metric values obtained by two competing algorithms.”
Rmind: A Tool for Cryptographically Secure Statistical Analysis (2016, methodology): “rank test [52] is a paired difference test.”
Towards Robust Toxic Content Classification (2019, methodology): “We have tested whether the toxicity rating of the unperturbed comment tended to be higher than that of the perturbed comment using the Wilcoxon signed rank test (Wilcoxon 1992) applied to pairs of unperturbed/perturbed toxicity scores averaged at the comment-level.”
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learning (2022, methodology): “Moreover, we perform statistical significance tests via a Wilcoxon rank-sum test (Wilcoxon, 1945) at a significance level of αwilcoxon = 0.05.”
Multi-fidelity classification using Gaussian processes: accelerating the prediction of large-scale computational models (2019, methodology): “Finally, we compare whether one classiﬁer is signiﬁcantly more accurate than the other using non-parametric Wilcoxon signed-rank test [45].”