2026· Far East Journal of Theoretical Statistics· Vol 70, pp. 485-500· 0 citations
Abstract
Motivated by sensitivity analysis in difference-in-differences, we study a median treatment contrast when the unobserved counterfactual cumulative distribution function (CDF) is allowed to lie within Kolmogorov distance \(M\) of a reference CDF. This restriction is distinct from the usual distributional parallel-trends assumption on untreated outcome changes, but it gives a transparent local sensitivity model around any chosen reference distribution. The identified set for the difference of marginal medians is a closed-form interval whose endpoints are quantiles of the reference distribution evaluated at > \(1 / 2 \pm M\). We give the joint asymptotic distribution of the plug-in endpoints and an Imbens-Manski-type confidence interval with pointwise asymptotic validity for fixed \(M\). The construction is computationally trivial, requires no bootstrap, and reduces to the standard quantile-based confidence interval for a difference of medians when \(M=0\). A Monte Carlo study verifies the variance formula numerically and assesses finite-sample coverage.
We study from a finite-sample viewpoint the problem of building tests and simultaneous confidence bands for cumulative distribution functions (CDFs), continuous or discrete. We emphasize procedures based on reweighted empirical distribution function (EDF) with shrinking bandwidths in the tails of the distribution. Sinc...
The median survival time is widely reported because it is robust and easily interpretable. Under right censoring, however, obtaining a confidence interval with two finite endpoints can be difficult. We present a Monte Carlo comparison of four confidence-interval procedures: the linear-scale Brookmeyer–Crowley interval,...
Randomization or design-based inference is becoming an increasingly popular tool for analysing data from randomized experiments: It does not require modelling assumptions on the distribution of outcomes or covariates, and hypothesis testing and estimation are respectively valid and unbiased in finite samples. Yet, conf...
We study preference elicitation under the Bradley-Terry-Luce (BTL) model where the true partworth vector is unknown and has to be estimated as a parameter with elicited preference information. The set of selected pairwise queries is non-uniform, deterministic, and arbitrary over a collection of alternatives, provided t...
The estimation of the extreme value index (EVI) plays a central role in extreme value theory (EVT), particularly in the statistical modelling of heavy-tailed phenomena. Classical semi-parametric estimators of the EVI are known to suffer from substantial bias, especially for moderate sample sizes and for common choices...
L. Henriques-Rodrigues, Frederico Caeiro, M. I. Gomes· 0 citations
We introduce a distribution-free goodness-of-fit test, termed the omega-1 test, which naturally complements the Kolmogorov--Smirnov test and Cram\'{e}r--von Mises test and can be viewed as their (piecewise) linear analog. Defined as an $\mathrm{L}^{1}$-functional of the empirical process, the test statistic improves on...
Wei-Xu Xia· 0 citations
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