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Designs for crossvalidating approximation models

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  • Qiong Zhang
  • Peter Z. G. Qian

Abstract

Multifold crossvalidation is routinely used for assessing the prediction error of an approximation model for a black-box function. Despite its popularity, this method is known to have high variability. To mitigate this drawback, we propose an experimental design approach that borrows Latin hypercube designs to construct a structured crossvalidation sample such that the input values in each fold achieve uniformity. Theoretical results show that the estimate of the prediction error of the proposed method has significantly smaller variability than its counterpart under independent and identically distributed sampling. Numerical examples corroborate the theoretical results. Copyright 2013, Oxford University Press.

Suggested Citation

  • Qiong Zhang & Peter Z. G. Qian, 2013. "Designs for crossvalidating approximation models," Biometrika, Biometrika Trust, vol. 100(4), pages 997-1004.
    • Handle: RePEc:oup:biomet:v:100:y:2013:i:4:p:997-1004
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    File URL: http://hdl.handle.net/10.1093/biomet/ast034
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    Cited by:

    1. Wang, Xiao-Lei & Zhao, Yu-Na & Yang, Jian-Feng & Liu, Min-Qian, 2017. "Construction of (nearly) orthogonal sliced Latin hypercube designs," Statistics & Probability Letters, Elsevier, vol. 125(C), pages 174-180.
    2. Ye Chen & Ilya O. Ryzhov, 2023. "Balancing Optimal Large Deviations in Sequential Selection," Management Science, INFORMS, vol. 69(6), pages 3457-3473, June.
    3. Diane Donovan & Benjamin Haaland & David J. Nott, 2017. "A simple approach to constructing quasi-Sudoku-based sliced space-filling designs," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(4), pages 865-878, August.

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