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Bayesian U-type design for nonparametric response surface prediction

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Abstract

This paper deals with Bayesian design over U-type designs of n runs and s factors with q levels for nonparametric response surface prediction. The criterion is developed in terms of the asymptotic approach of Mitchell et al. (Ann Statist 22: 634–651, 1994) for a specific covariance kernel. An optimal design is given in approximate design theory over the all level combinations. A connection with orthogonality and aberration is established. A lower bound for the criterion is provided, and numerical results show that this lower bound is tight.

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Authors and Affiliations

  1. Department of Mathematics of Shanghai Normal University, Scientific Computing Key Laboratory of Shanghai Universities, 200234, Shanghai, China

    Rong-Xian Yue

  2. Division of Scientific Computation of E-Institute of Shanghai Universities, 200234, Shanghai, China

    Rong-Xian Yue

  3. Department of Statistics, Visva-Bharati University, Santiniketan, India

    Kashinath Chatterjee

Authors
  1. Rong-Xian Yue
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  2. Kashinath Chatterjee
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Correspondence to Rong-Xian Yue.

Additional information

This work was partially supported by NSFC grant (10671129), Shanghai Normal University Leading Academic Discipline Project (DZL805), Special Funds for Doctoral Authorities of Education Ministry (20060270002), E-Institutes of Shanghai Municipal Education Commission (E03004) and Science and Technology Commission of Shanghai Municipality grant (075105118).

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Yue, RX., Chatterjee, K. Bayesian U-type design for nonparametric response surface prediction. Metrika 72, 219–231 (2010). https://doi.org/10.1007/s00184-009-0249-0

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  • DOI: https://doi.org/10.1007/s00184-009-0249-0

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