Empirical Probability Plots and Statistical Inference for Nonlinear Models in the Two-Sample Case

By Kjell A. Doksum. 1974

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

The Econometrics of Randomized Experiments (2016, methodology): “For that statistic one can calculate the exact p-value under the null hypothesis that there is no effect of the treatment whatsoever using the Fisher randomization distribution. See for example Young (2016). Alternatively one can use adjustments to the pvalues to take account of the multiple testing”
Nonparametric Tests for Treatment Effect Heterogeneity (2008, neutral): “3 See, for example, Lehmann (1974) Doksum (1974) , Firpo (2004), Abadie, Angrist and Imbens (2002), Cher-”
SHARP BOUNDS ON THE DISTRIBUTION OF TREATMENT EFFECTS AND THEIR STATISTICAL INFERENCE (2009, background): “1 in Embrechts et al. (2003). In particular, they showed that for a real-valued random variable Z and a function φ increasing and left-continuous on the range of Z , it holds that the quantile of φ(Z) at quantile level q is given by φ ( F−1 Z (q) ) , where FZ is the distribution function of Z .”
Three-Step Censored Quantile Regression and Extramarital Affairs (2002, methodology): “We illustrate the procedure with an extramarital affairs example.”
Quantile Regression: Name Index (2005, unknown): “Doksum 1974 shows that if x is deened as the horizontal distance" between F and G at x, so Fx = Gx + x then x is uniquely deened and can be expressed as x = G ,1 F x , x: Changing variables so = Fx one may deene the quantile treatment eeect, = F ,1 = G ,1 , F ,1 : In the two sample setting this…”
Efficient Semiparametric Estimation of Quantile Treatment Effects (2004, background): “As originally dened by Lehmann (1974) and Doksum (1974), the QTE corresponds, for any xed percentile, to the horizontal distance between two cumulative distribution functions. In dening QTE as a treatment e¤ect at the individual level, both Doksum (1974) and Lehmann (1974) implicitly argued that ”
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.”
Instrumental quantile regression inference for structural and treatment effect models (2006, background): “This model has played a fundamental role in statistics at least since Bhattacharya (1963), Lehmann (1974), and Doksum (1974).”
Linear quantile mixed models (2013, methodology): “The analysis of the so-called Lehman-Doksum quantile treatment effect (Doksum 1974; Lehmann 1975; Koenker and Xiao 2002) has particular relevance in placebo-controlled studies as the one considered here.”
The Gender Gap between Earnings Distributions (2019, background): “This can be a nonseparable function of observable characteristics x and unobservable disturbances u, normalized and typically interpreted as ability (Doksum 1974; Chernozhukov and Hansen 2008).18 Unobservables u are the rank variable or quantile and thus can be fixed in estimations.”
PARTIAL IDENTIFICATION OF THE DISTRIBUTION OF TREATMENT EFFECTS AND ITS CONFIDENCE SETS (2009, unknown): “Doksum (1974) and Lehmann (1974) used ½F 11 ðF0ðy0ÞÞ y0 to measure treatment effects.”
Instrumental Variable Quantile Regression (2004, methodology): “These QTE may then be used to summarize the impact of variables of interest D on the quantiles of potential outcomes as suggested in Doksum (1974) and Lehmann (1975).”
Asymptotic Distribution Theory of Empirical Rank-dependent Measures of Inequality (2005, methodology): “To this end we utilize the close formal connection between the shift function of Doksum (1974) and the Lorenz curve4.”
Comparison of multivariate distributions using quantile-quantile plots and related tests (2014, background): “As discussed in Doksum [12], Doksum and Sievers [13] and Koenker ([23], pages 31 and 32), there are some fundamental connections between the Q–”
Quantile Probability and Statistical Data Modeling (2004, background): “Of related interest is the shift function (x) = G−1(F (x)) − x studied by Doksum (1974).”