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Optimal tightening of the KWW joint confidence region for a ranking

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  • Wright, Tommy

Abstract

Klein, Wright, and Wieczorek (2020), hereafter KWW, constructs a simple novel measure of uncertainty for an estimated ranking using a joint confidence region for the true ranking of K populations. In this current paper, our proposed framework permits some control over the amount of uncertainty and tightness in various portions of the estimated ranking with an optimal allocation of sample among the K populations.

Suggested Citation

  • Wright, Tommy, 2025. "Optimal tightening of the KWW joint confidence region for a ranking," Statistics & Probability Letters, Elsevier, vol. 217(C).
    • Handle: RePEc:eee:stapro:v:217:y:2025:i:c:s0167715224002578
    DOI: 10.1016/j.spl.2024.110288
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    References listed on IDEAS

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    1. Wright, Tommy, 2017. "Exact optimal sample allocation: More efficient than Neyman," Statistics & Probability Letters, Elsevier, vol. 129(C), pages 50-57.
    2. Martin Klein & Tommy Wright & Jerzy Wieczorek, 2020. "A joint confidence region for an overall ranking of populations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(3), pages 589-606, June.
    3. Wright, Tommy, 2020. "A general exact optimal sample allocation algorithm: With bounded cost and bounded sample sizes," Statistics & Probability Letters, Elsevier, vol. 165(C).
    4. Magne Mogstad & Joseph P Romano & Azeem M Shaikh & Daniel Wilhelm, 2024. "Inference for Ranks with Applications to Mobility across Neighbourhoods and Academic Achievement across Countries," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 91(1), pages 476-518.
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