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Applications of Using Quaternary Codes to Nonregular Designs

Applications of Using Quaternary Codes to Nonregular Designs. Frederick K.H. Phoa Department of Statistics University of California at Los Angeles 07/12/2006 International Conference on Design of Experiments and its Applications Email: fredphoa@stat.ucla.edu.

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Applications of Using Quaternary Codes to Nonregular Designs

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  1. Applications of Using Quaternary Codes to Nonregular Designs Frederick K.H. Phoa Department of Statistics University of California at Los Angeles 07/12/2006 International Conference on Design of Experiments and its Applications Email: fredphoa@stat.ucla.edu Research supported by NSF DMS Grant

  2. Content • Notations and Definitions on Nonregular Designs • Basics on Quaternary Codes • Motivation of using Quaternary Codes. • Previous Result I: Design Construction with Resolution 3.5 • New Application I: Design Construction with Resolution 4.0 • Previous Result II: Fast Search Results on Optimum 2n-2Designs • New Application II: Fast Search Results on Optimum 2n-4Designs • Future Extensions • Conclusion

  3. Notations and Definitions Let D be a design of N runs and n factors. J-characteristics (Jk) of D: Let a subset of k columns of D. Then When D is a regular design, Generalized Resolution of D: Suppose that r is the smallest integer such that . Then we define the generalized resolution of D.

  4. Basics on Quaternary (Z4) Codes Algorithms of using Z4-codes to generate nonregular designs: • Let G (generator matrix) be an r x n full-rank matrix over Z4. • A 4-level design C is formed based on G. • Applying , a 2-level design D is formed based on C. Gray map Bijection Transformation of Gray Map:

  5. Basics on Quaternary (Z4) Codes Example: 1. Given a 2 x 4 generator matrix: 2. A 4-level design C is formed based on G. 3. A 2-level design D is formed based on C through Gray map. Generalized Resolution = 4.0 (GOOD DESIGN)

  6. Motivation of using Quaternary Codes Application of Nordstrom and Robinson Code: Hedayat, Sloane and Stufken (1999); Xu (2005) It can form a 256 runs, 16 columns designs. • Generalized Resolution = 6.5 • Minimum Runs to achieve this in Regular Design = 512 runs Why Z4?

  7. Previous Result I:Design Construction with Resolution 3.5 [Xu and Wong, accepted by Statistica (2005)] Example - 16 runs design: From the list of all possible columns: Step #1: Delete columns that do not contain any 1’s Step #2: Delete columns whose first non-zero and non-two entry are 3’s. Result: Generalized Resolution = 3.5

  8. New Applications I:Design Construction with Resolution 4.0 [Phoa, contributed talk in JRC Tennessee, USA (2006)] Additional Step on Previous Algorithm: Result from Previous Steps: Step #3: Delete columns whose sum of columns entries are even. Result: Generalized Resolution = 4.0

  9. New Applications I:Design Construction with Resolution 4.0 Maximum Columns of Design with GR=4.0: This table suggests that the maximum columns of design with GR =4.0 is N/2 with N runs. Previous complete search algorithm breaks down when N>256. This construction generalizes the case for higher run sizes.

  10. Previous Result II:Fast Search Results on Optimum 2n-2Designs [Phoa, contributed talk in JRC Tennessee, USA (2006)] Structure for Optimum (over Z4) 2n-2 Designs: Notation: G = Ik(a,b) design where k = size of Identity matrix a = number of 1’s in the last column b = number of 2’s in the last column Fast Calculation on GR of 2n-2 Design: 1. If b ≥ a, then GR = 2(a+1). 2. If b < a, then for even design, GR = min{2b+a+1 , 2(a+1)} + decimals. for odd design, GR = min{2b+a , 2(a+1)} + decimals. 3.

  11. Previous Result II:Fast Search Results on Optimum 2n-2Designs Search Results on Optimum Designs of different runs: • This search method bypasses the actual calculation of J-characteristics • wordlength pattern • Computer time on searching the optimum designs of particular run size is much less than the traditional methods  Optimality of exceptionally large design is possible

  12. New Applications II:Fast Search Results on Optimum 2n-4Designs Structure for Optimum (over Z4) 2n-4 Even Designs: Dimension of G = k x (k+2). Dimension of D = 4k x 2(k+2) where k = size of Identity matrix v1 = First 4-level column in G v2 = Second 4-level column in G We count the number of combination of each row of v1 and v2 using a counting matrix c with the following notation: where cab = number of row combination with v1i = a and v2i = b a, b = {0,1,2,3}

  13. New Applications II:Fast Search Results on Optimum 2n-4Designs Fast Calculation on GR of 2n-4 Design: GR = min{ GRv12, GRv1, GRv2 } + decimals where

  14. New Applications II:Fast Search Results on Optimum 2n-4Designs Fast Calculation on GR of 2n-4 Even Design: The decimal depends on which equation the GR comes from: If it comes from the 3rd equation of either GRv12, GRv1 or GRv2, If it comes from the 1st or 2nd equation of either GRv12, GRv1 or GRv2, where if GR=GRv12 if GR=GRv1 if GR=GRv2

  15. New Applications II:Fast Search Results on Optimum 2n-4Designs Example – 256 run Design: Maximum Resolution of Regular Design with 256 run  6.0

  16. New Applications II:Fast Search Results on Optimum 2n-4Designs Search Results on Optimum Designs of different runs: • Advantages of this Fast Search Method: • It saves tremendous amount of computer search time. • It is possible to search for design with super large run size. • The generalized resolution of our best results beats the regular design with same run size.

  17. Future Extensions 1. Construction of Designs with higher Resolutions.  Algorithm on GR = 4.5 Construction  Designs Constructions with properties similar to Nordstrom-Robinson Design. 2. Extensions of Fast Search Method.  Optimality of 2n-6 Design.  Analog to the Odd Design. 3. The Ultimate Goal: Develop a Generalization on current method of calculating GR of any given designs, which isbrand new, fast and clean for both Optimum Search and Construction purpose.

  18. Conclusion • Motivation: Nordstrom and Robinson code shows the potential of using quaternary code in designs of experiment. • A new construction method generates the design with GR=4.0, an extension from the previous GR=3.5 • A new search method provides some new results to fast search for the optimum 2n-4 designs, a generalization of its previous special case with 2n-2 runs. • Future extensions on the design construction, fast search method are suggested.

  19. Acknowledgements Professor Hongquan Xu UCLA Department of Statistics for his valuable and fruitful comments and discussions. References • Deng, L.Y. and Tang, B (1999). Generalized resolution and minimum aberration criteria for Plackett-Burman and other nonregular factorial designs, Statistica Sinica, 9, 1071 – 1082. • Hedayat, A.S., Sloane, N.J.A. and Stufken, J. (1999) Orthogonal Arrays: Theory and Applications, Springer, New York. • Xu, H. (2005) Some nonregular designs from the Nordstrom and Robinson code and their statistical properties, Biometrika, 92, 385 – 397. • Xu, H. and Wong, A. (2005) Two-level Nonregular designs from quaternary linear codes, accepted by Statistica Sinica. • Phoa, F (2006) Applications of Quaternary Codes on Nonregular Design, contributed talk in JRC on Statistics in Quality, Industry and Technology, Knoxville, TN, USA

  20. Thank you very much for your attention! Question? How many times I appear in this talk?

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