Asymptotic theory of the quadratic assignment procedure for dyadic data analysis

Clicks: 10
ID: 337327
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #153 of 153 articles by views in Journal of the Royal Statistical Society Series B (Statistical Methodology)

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 153 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Abstract The quadratic assignment procedure (QAP) is a popular tool for analysing dyadic data in medical and social sciences. To test the association between 2 dyadic measurements represented by 2 symmetric matrices, QAP calculates the p-value by permuting the units, or equivalently, by permuting the rows and columns of 1 matrix in the same way. Its extension to the regression setting, known as the multiple regression QAP, has also gained popularity, especially in psychometrics. However, the statistical theory for QAP has not been fully established in the literature. We fill the gap in this paper. We formulate the dyadic models underlying various QAPs. We derive (a) the asymptotic sampling distributions of some canonical test statistics and (b) the corresponding asymptotic permutation distributions induced by QAP under strong and weak null hypotheses. Task (a) relies on applying the theory of U-statistics, and task (b) relies on applying the theory of double-indexed permutation statistics. Our asymptotic theory combines tasks (a) and (b), and suggests that we must use properly studentized statistics in QAP to ensure that it is finite-sample exact under the strong null hypothesis and preserves the asymptotic type I error rate under the weak null hypothesis.
Reference Key
openalex_W4404350848 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Lei Shi, Peng Ding
Are you one of these authors? Claim your profile
Journal Journal of the Royal Statistical Society Series B (Statistical Methodology)
Year 2026
DOI
10.1093/jrsssb/qkag136
URL
https://doi.org/10.1093/jrsssb/qkag136
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.