EconBase
← All papers

Inference for high-dimensional exchangeable arrays

Harold D. Chiang, Kengo Kato, Yuya Sasaki

arXiv 10 Sep 2020 · Econometrics · publishedJournal of the American Statistical Association (2021) · 3 citations (OpenAlex)

arXiv:2009.05150 · PDF · DOI · OpenAlex · Extracted main text

Abstract

We consider inference for high-dimensional separately and jointly exchangeable arrays where the dimensions may be much larger than the sample sizes. For both exchangeable arrays, we first derive high-dimensional central limit theorems over the rectangles and subsequently develop novel multiplier bootstraps with theoretical guarantees. These theoretical results rely on new technical tools such as Hoeffding-type decomposition and maximal inequalities for the degenerate components in the Hoeffiding-type decomposition for the exchangeable arrays. We exhibit applications of our methods to uniform confidence bands for density estimation under joint exchangeability and penalty choice for $\ell_1$-penalized regression under separate exchangeability. Extensive simulations demonstrate precise uniform coverage rates. We illustrate by constructing uniform confidence bands for international trade network densities.

Citation extraction

68
references
145
in-text mentions
68
distinct cited
1
self-citations
11,076
main-text words

appendix boundary found by appendix_command · 33% of the source is main text. Read the extracted text to check this.

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
1Graham, B. S., F. Niu, and J. L. Powell (2019) Kernel density estimation for undirected dyadic data1.00053100%
2Kallenberg, O (2006) Probabilistic Symmetries and Invariance Principles1.00053100%
3Silverman, B. W (1976) Limit theorems for dissociated random variables0.87462100%
4Davezies, L., X. D'Haultfoeuille, and Y. Guyonvarch (2020) Empirical process results for exchangeable arrays0.77615447%
5Chernozhukov, V., D. Chetverikov, K. Kato, and Y. Koike (2019) Improved central limit theorem and bootstrap approximations in high dimensions0.7373367%
6Graham, B. S., F. Niu, and J. L. Powell (2020) Minimax risk and uniform convergence rates for nonparametric dyadic regression0.73732100%
7Menzel, K (2017) Bootstrap with clustering in two or more dimensions0.73732100%
8Chen, X. and K. Kato (2020) Jackknife multiplier bootstrap: finite sample approximations to the $U$-process supremum with applications0.6443267%
9Andrews, D. W (2005) Cross-section regression with common shocks0.64422100%
10Head, K. and T. Mayer (2014) Gravity equations: Workhorse, toolkit, and cookbook0.64422100%

Showing the top 10 of 68 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
1Maximal Inequalities for Separately Exchangeable Empirical Processes0.96193
2Cross-Fitting-Free Debiased Machine Learning with Multiway Dependence0.888105
3Dyadic Double/Debiased Machine Learning for Analyzing Determinants of Free Trade Agreements0.79464
4Gaussian Approximation for Maximum Score and Non-Smooth M-Estimators with Multiway Dependence0.73732
5Estimation and Inference for Causal Functions with Multiway Clustered Data0.714115
6Post-selection inference for network structure 10.64422
7Algorithmic Subsampling under Multiway Clustering0.51121
8Multiway empirical likelihood0.51122
9Standard Errors for Two-Way Clustering with Serially Correlated Time Effects0.51121
10Empirical likelihood and uniform convergence rates for dyadic kernel density estimation0.40511