Nonparametrics: Statistical Methods Based on Ranks

By Erich Leo Lehmann, H. D'Abrera. 1975

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “uantile treatment eﬀect as the diﬀerence in quantiles between the Yi(1) and Yi(0) distributions: τs = qY (1)(s)− qY (0)(s). (4.9) Such quantile treatment eﬀects have been studied in Doksum (1974) and Lehman (1974), and more recently in Abadie, Angrist and Imbens (2002), Chernozhukov and Hansen (2005”
Statistical Analysis in Climate Research (2000, unknown): “, Draper and Smith [104] or Lehmann and D’Abrera [249]) to determine whether the residuals change sign less frequently (i.”
Distributed Statistical Estimation and Rates of Convergence in Normal Approximation (2017, methodology): “For instance, when card( J ) = 2 and ¯ θ J = is the well-known Hodges-Lehmann estimator of the location parameter, see (Hodges and Lehmann, 1963; Lehmann and D’Abrera, 2006); for a comprehensive study of U-quantiles, see (Arcones, 1996).”
Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization (2009, result): “↓ indicates statistically significantly worse performance that the best result, according to a Wilcoxon rank sum test (p 0.01) [10].”
Quantile Regression: Name Index (2005, unknown): “In place of the classical Fisherian experimental design model in which the treatment induces a simple location shift of the response distribution, Lehmann 1974 proposed the following general model of treatment response: Suppose the treatment adds the amount x when the response of the untreated…”
Similarity measures for spectral discrimination of salt‐affected soils (2007, methodology): “The Mann–Whitney U-test is recommended, because it can be used with various types of data, is ideally dependent on random selection, it can be used for small sample sets (between 5 and 20), and is a nonparametric test whereby normal distribution of data is not necessary (Lehmann 1998).”
Quantile Treatment Effects of College Quality on Earnings: Evidence from Administrative Data in Texas. NBER Working Paper No. 18068. (2012, background): “But, the main qualitative and quantitative results remain, with the returns for UT-Austin students increasing across the income distribution and the returns for Texas A&M students declining.(27) For community college attendees, there is more of a diverge from the baseline estimates.”
Measuring reproducibility of high-throughput experiments (2011, background): “Though using ranks, instead of the raw values of scores, generally causes some loss of information, this loss is known to be asymptotically negligible [Lehmann (2006)].”