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A Unified Nonparametric Test of Transformations on Distribution Functions with Nuisance Parameters

Xingyu Li, Xiaojun Song, Zhenting Sun

arXiv 20 Feb 2022 · Statistics — Methodology · 1 citations (OpenAlex)

arXiv:2202.11031 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper proposes a simple unified approach to testing transformations on cumulative distribution functions (CDFs) in the presence of nuisance parameters. The proposed test is constructed based on a new characterization that avoids the estimation of nuisance parameters. The critical values are obtained through a numerical bootstrap method which can easily be implemented in practice. Under suitable conditions, the proposed test is shown to be asymptotically size controlled and consistent. The local power property of the test is established. Finally, Monte Carlo simulations and an empirical study show that the test performs well on finite samples.

Citation extraction

64
references
189
in-text mentions
70
distinct cited
2
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main-text words

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Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Jun, S. J. and Pinkse, J (2009) Semiparametric tests of conditional moment restrictions under weak or partial identification0.874202100%
2Hong, H. and Li, J (2018) The numerical delta method0.8434375%
3Radulović, D (1996) The bootstrap for empirical processes based on stationary observations0.8115280%
4Chen, Q. and Fang, Z (2019) Inference on functionals under first order degeneracy0.67526431%
5Arcones, M. A. and Yu, B (1994) Central limit theorems for empirical and U-processes of stationary mixing sequences0.6444250%
6Chung, E. and Olivares, M (2021) Permutation test for heterogeneous treatment effects with a nuisance parameter0.6443267%
7Kosorok, M. R (2008) Introduction to Empirical Processes and Semiparametric Inference0.56711318%
8van der Vaart, A. W. and Wellner, J. A (1996) Weak Convergence and Empirical Processes0.52834515%
9Fang, Z. and Santos, A (2019) Inference on directionally differentiable functions0.5114225%
10Lehmann, E. L. and Romano, J. P (2005) Testing Statistical Hypotheses0.5112250%

Showing the top 10 of 70 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1A Nonparametric Test of $m$th-degree Inverse Stochastic Dominance0.40511