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Bias reduction via complementation of orthogonal arrays under a baseline parameterization

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  • Karunanayaka, Ruwan Chamara

Abstract

Under a baseline parameterization, minimum aberration orthogonal arrays minimize variance but not bias, while one-factor-at-a-time designs minimize bias but not variance. A new class of compromise designs is constructed by complementing and modifying minimum aberration arrays. The resulting information matrix is shown to have a compound-symmetric structure, and closed-form expressions are derived for the As-, Ds-, and Es-efficiency criteria. Simultaneous optimality under all three criteria is established within the class. A decomposition of the bias difference K2(ZC2∗)−K2(ZMA) into pairwise contributions reveals a sign-reversal mechanism in the alias matrix, and bias reduction relative to the parent design is proved for all regular fractions. Numerical results show bias reductions of 14%–49% with efficiency losses of only 1%–10%.

Suggested Citation

  • Karunanayaka, Ruwan Chamara, 2026. "Bias reduction via complementation of orthogonal arrays under a baseline parameterization," Statistics & Probability Letters, Elsevier, vol. 237(C).
    • Handle: RePEc:eee:stapro:v:237:y:2026:i:c:s0167715226001835
    DOI: 10.1016/j.spl.2026.110819
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