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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.15057 |
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| _version_ | 1866908717899841536 |
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| author | Mohammadi, Ehsan Chen, Fanghua Cai, Yizhou Yang, Yun Ma, Ting Fung Zhou, Lu |
| author_facet | Mohammadi, Ehsan Chen, Fanghua Cai, Yizhou Yang, Yun Ma, Ting Fung Zhou, Lu |
| contents | The Stratified Bootstrap Test (SBT) provides a nonparametric, resampling-based framework for assessing the stability of group-specific ranking patterns in multivariate survey or rating data. By repeatedly resampling observations and examining whether a group's top-ranked items remain among the highest-scoring categories across bootstrap samples, SBT quantifies ranking robustness through a non-containment index. In parallel, the stratified bootstrap test extends this framework to formal statistical inference by testing ordering hypotheses among population means. Through resampling within groups, the method approximates the null distribution of ranking-based test statistics without relying on distributional assumptions. Together, these techniques enable both descriptive and inferential evaluation of ranking consistency, detection of aberrant or adversarial response patterns, and rigorous comparison of groups in applications such as survey analysis, item response assessment, and fairness auditing in AI systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_15057 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Stratified Bootstrap Test Package Mohammadi, Ehsan Chen, Fanghua Cai, Yizhou Yang, Yun Ma, Ting Fung Zhou, Lu Methodology Computation The Stratified Bootstrap Test (SBT) provides a nonparametric, resampling-based framework for assessing the stability of group-specific ranking patterns in multivariate survey or rating data. By repeatedly resampling observations and examining whether a group's top-ranked items remain among the highest-scoring categories across bootstrap samples, SBT quantifies ranking robustness through a non-containment index. In parallel, the stratified bootstrap test extends this framework to formal statistical inference by testing ordering hypotheses among population means. Through resampling within groups, the method approximates the null distribution of ranking-based test statistics without relying on distributional assumptions. Together, these techniques enable both descriptive and inferential evaluation of ranking consistency, detection of aberrant or adversarial response patterns, and rigorous comparison of groups in applications such as survey analysis, item response assessment, and fairness auditing in AI systems. |
| title | Stratified Bootstrap Test Package |
| topic | Methodology Computation |
| url | https://arxiv.org/abs/2512.15057 |