Weiqi Yang, Weiwei Zhuang, Xiaojun Song
arXiv 18 Sep 2026 · Statistics — Methodology
arXiv:2609.21622 · PDF · Extracted main text
Comparing two populations at the same physical covariate value requires more than conditional means or isolated target-point decisions: researchers may need evidence about an entire conditional-distribution ordering over a continuum, even when covariate margins differ. This paper makes that common-value comparison estimable under an explicit structure--flexibility tradeoff and turns the resulting surface into simultaneous evidence for first-order stochastic dominance. Population-specific margins map the common covariate value into each group, while a fitted copula-derivative representation links conditional distributions across the region. Uniform inference propagates uncertainty from both the margins and dependence model through a one-sided statistic with unknown binding locations. Under correct specification within a finite copula class, smoothness and trimming conditions, and a uniquely best candidate family, the procedure admits uniform control and consistent calibration. Simulations show increasing rejection as alternatives become more distinguishable, alongside model-selection sensitivity and small-sample size distortion. In a descriptive PSID application, the high--low parental-education comparison satisfies the two-direction criterion after multiplicity adjustment, whereas adjacent education-group comparisons remain inconclusive. The framework therefore supports region-wide distributional comparison while making its structural and inferential boundaries explicit.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Tsukahara, Hideatsu (2005) Semiparametric estimation in copula models | 0.644 | 2 | 2 | 100% |
| 2 | Fang, Z. and Santos, A (2019) Inference on directionally differentiable functions | 0.511 | 2 | 2 | 50% |
| 3 | Præstgaard, John and Wellner, Jon A (1993) Exchangeably Weighted Bootstraps of the General Empirical Process | 0.511 | 2 | 2 | 50% |
| 4 | Federico A. Bugni and Ivan A. Canay and Deborah Kim (2025) Testing Conditional Stochastic Dominance at Target Points | 0.511 | 2 | 1 | 100% |
| 5 | Chang, M. and Lee, S. and Whang, Y.-J (2015) Nonparametric Tests of Conditional Treatment Effects with an Application to Single-Sex Schooling on Academic Achievements | 0.511 | 2 | 1 | 100% |
| 6 | Miguel A. Delgado and Juan Carlos Escanciano (2013) Conditional Stochastic Dominance Testing | 0.511 | 2 | 1 | 100% |
| 7 | Matt Goldman and David M. Kaplan (2018) Comparing Distributions by Multiple Testing across Quantiles or CDF Values | 0.511 | 2 | 1 | 100% |
| 8 | Jesus Gonzalo and Jose Olmo (2014) Conditional Stochastic Dominance Tests in Dynamic Settings | 0.511 | 2 | 1 | 100% |
| 9 | Oliver Linton and Myung Hwan Seo and Yoon-Jae Whang (2023) Testing Stochastic Dominance with Many Conditioning Variables | 0.511 | 2 | 1 | 100% |
| 10 | Oliver Linton and Kyungchul Song and Yoon-Jae Whang (2010) An Improved Bootstrap Test of Stochastic Dominance | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 27 scored citations.