主講人:Prof.Ming-Chung Chang (張明中)

Institute of Statistical Science, Academia Sinica

 題:The selection of efficient multi-stratum factorial designs: a Bayesian approach

  期:1061114日(星期二)

 間:下午14:00

  點:科學館S433

Multi-stratum factorial designs are quite common in industrial investigations; a simple example is the so-called split-plot designs. In a multi-stratum experiment, there are multiple error terms (strata) with different variances that arise from complicated structures of the experimental units. For unstructured (single-stratum) experimental units, minimum aberration is a popular criterion for choosing fractional factorial designs. Many works in the literature extended this criterion to multi-stratum units. One difficulty is that the formulation of a wordlength pattern based on which minimum aberration is defined requires an order of desirability among the relevant words, but a natural order is often lacking. Furthermore, a criterion based only on wordlength patterns does not account for the different stratum variances. Chang and Cheng (2017), which presents a study of optimal and efficient two-level multi-stratum factorial designs via a Bayesian approach, overcomes this difficulty and works for general nonregular/nonorthogonal designs. In addition to the standard Bayesian A- and D-optimal criteria, a more manageable two-stage surrogate criterion is derived and a useful tool for eliminating inferior designs is developed. In this talk, I will give a detailed account of Chang and Cheng (2017), including some preliminary materials about multi-stratum designs, and use an example to illustrate the theory.

        Keywords: block structure, Bayesian optimality, wordlength pattern.

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