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Confounding analysis of s-level designs with multi-block ...
[Submitted on 23 Jun 2026] · 2026-06-24 · via stat updates on arXiv.org

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Abstract:In practical experiments, block variables often arise from multiple sources of heterogeneity. To address the confounding problem, this paper proposes a blocked aliased component-number pattern (B$^2$-ACNP) to analyze the confounding properties of s-level designs with multi-block variables. We calculate the values of (B$^2$-ACNP) via a blocked wordlength distribution matrix. The classification patterns of existing criteria can be expressed as functions of specific elements within the B$^2$-ACNP, thereby stablishing connections within a unified framework. Further, we provide confounding algorithms and visualization methods of the B$^2$-ACNP. Finally, case analysis clarifies the significant role of the B$^2$-ACNP. The Python code is available in the Appendix.

Submission history

From: Zhiming Li [view email]
[v1] Tue, 23 Jun 2026 04:06:14 UTC (3,241 KB)