Nonparametrics Statistical Methods Based On Ranks

By Marie Schmidt. 2016

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Vector Quantile Regression (2014, background): “Another motivation is related to the classical definition of quantile treatment effects introduced by [26], and further developed by [10], [17], and others.”
Fast Implementation of DeLong’s Algorithm for Comparing the Areas Under Correlated Receiver Operating Characteristic Curves (2014, background): “(16) Remark 1: Note that (16) has been mentioned in passing in Lehmann’s book [26].”
Describing Populations and Samples in Doctoral Student Research (2021, background): “A sample size that is 15% greater than the calculation for the parametric a priori power analysis provides the equivalent statistical power for the associated non-parametric test (Lehmann, 1973).”
Bridging Finite and Super Population Causal Inference (2017, unknown): “This connection becomes more apparent when only the ranks of the outcomes are used to construct the test statistics, as discussed extensively by Lehmann (1975).”
Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling (2014, methodology): “Note that we chose the Spearman rank correlation [35] instead of the usual Pearson’s.”
Fréchet regression for random objects with Euclidean predictors (2016, methodology): “In order to obtain a p-value, a permutation test can be performed (Lehmann and D’Abrera 2006; Higgins 2004; Bradley 1968).”