Computer-Aided Design of Experiments—Some Practical Experiences

References

• Atkinson, A. C. (1982). “Developments in the Design of Experiments”. International Statistical Review 50, pp. 161–177.
   Web of Science ®Google Scholar
• Atwood, C. L. (1969). “Optimal and Efficient Designs of Experiments”. Annals of Mathematical Statistics 40, pp. 1570–1602.
   Google Scholar
• Box, G. E. P. and Draper, N. R. (1975). “Robust Designs”. Biometrika 62, pp. 347–352.
   Web of Science ®Google Scholar
• Box, G. E. P.; Hunter, W. G.; and Hunter, J. S. (1978) Statistics for Experimenters. John Wiley and Sons, Inc., New York, NY.
   Google Scholar
• Box, G. E. P. and Hunter, W. G. (1963). “Sequential Design of Experiments for Nonlinear Models”. Proceedings of the IBM Scientific Computer Symposium on Statistics, pp. 113–137.
   Google Scholar
• Box, G. E. P. and Hunter, W. G. (1965). “The Experimental Study of Physical Mechanisms”. Technometrics 7, pp. 23–42.
   Web of Science ®Google Scholar
• Cook, R. D. and Nachtscheim, C. J. (1980). “A Comparison of Algorithms for Constructing Exact D-optimal Designs”. Technometrics 22, pp. 315–324.
   Web of Science ®Google Scholar
• Draper, N. R. and Smith, H., Jr. (1981). Applied Regression Analysis, 2nd ed. John Wiley and Sons, Inc., New York, NY.
   Google Scholar
• Dykstra, O. Jr., (1971). “The Augmentation of Experimental Data to Maximize |X′X|”. Technometrics 13, pp. 682–688.
   Web of Science ®Google Scholar
• Evans, J. W. (1979). “Computer Augmentation of Experimental Designs to Maximize |X′X|”. Technometrics 21, pp. 321–330.
   Web of Science ®Google Scholar
• Galil, Z. and Kiefer, J. (1980). “Time- and Space-Saving Computer Methods, Related to Mitchell's DETMAX, for Finding D-optimum Designs”. Technometrics 22, pp. 301–313.
   Web of Science ®Google Scholar
• Gaylor, D. W. and Merrill, J. A. (1968). “Augmenting Existing Data in Multiple Regression”. Technometrics 10, pp. 73–81.
   Web of Science ®Google Scholar
• Hahn, G. J. (1985). “More Intelligent Statistical Software and Statistical Expert Systems: Future Directions (With Discussion)”. The American Statistician 39, pp. 1–16.
   Web of Science ®Google Scholar
• Hahn, G. J.; Feder, P. I.; and Meeker, W. Q. (1976). “The Evaluation and Comparison of Experimental Designs for Fitting Regression Functions”. Journal of Quality Technology 8, pp. 140–157.
   Google Scholar
• Hahn, G. J.; Feder, P. I.; and Meeker, W. Q. (1978). “Evaluating the Effect of Incorrect Specification of a Regression Model”. Journal of Quality Technology 10, pp. 61–72; 93–98.
   Web of Science ®Google Scholar
• Hebble, T. L. and Mitchell, T. J. (1972). “Repairing” Response Surface Designs”. Technometrics 14, pp. 767–779.
   Web of Science ®Google Scholar
• Hillyer, M. J. and Roth, P. M. (1972). “Planning of Experiments When the Experimental Region is Constrained. Application of Linear Transformations to Factorial Design”. Chemical Engineering Science 27, pp. 187–197.
   Web of Science ®Google Scholar
• Juusola, J. A.; Bacon, D. W.; and Downie, J. (1972). “Sequential Statistical Design Strategy in an Experimental Kinetic Study”. The Canadian Journal of Chemical Engineering 50, pp. 796–801.
   Web of Science ®Google Scholar
• Keifer, J. (1961). “Optimum Designs in Regression Problems, II”. Annals of Mathematical Statistics 32, pp. 298–325.
   Google Scholar
• Keifer, J. and Wolfowitz, J. (1960). “The Equivalence of Two Extremum Problems”. Canadian Journal of Mathematics 12, pp. 363–366.
   Google Scholar
• Kennard, R. W. and Stone, L. (1969). “Computer Aided Design of Experiments”. Technometrics 11, pp. 137–148.
   Web of Science ®Google Scholar
• Marquardt, D. W. and Snee, R. D. (1975). “Ridge Regression in Practice. The American Statistician 29, pp. 3–19.
   Web of Science ®Google Scholar
• Mitchell, T. J. (1974). “An Algorithm for the Construction of D-optimal Experimental Designs”. Technometrics 16, pp. 203–210.
   Web of Science ®Google Scholar
• Mitchell, T. J. and Bayne, C. K. (1978). “D-optimal Fractions of Three-Level Factorial Designs (With Discussion)”. Technometrics 20, pp. 369–383.
   Web of Science ®Google Scholar
• Morris, M. D. and Mitchell, T. J. (1983). “Two-Level Multifactor Designs for Detecting the Presence of Interactions”. Technometrics 25, pp. 345–355.
   Web of Science ®Google Scholar
• Nigam, A. K.; Gupta, S. C.; and Gupta, S. (1983). “A New Algorithm for Extreme Vertices Designs for Linear Mixture Models”. Technometrics 25, pp. 367–371.
   Web of Science ®Google Scholar
• Snee, R. D. (1971). “Design and Analysis of Mixture Experiments”. Journal of Quality Technology 3, pp. 159–169.
   Google Scholar
• Snee, R. D. (1973a). “Some Aspects of Nonorthogonal Data Analysis—Part I. Developing Prediction Equations”. Journal of Quality Technology 5, pp. 67–69.
   Google Scholar
• Snee, R. D. (1973b). “Some Aspects of Nonorthogonal Data Analysis—Part II. Comparison of Means”. Journal of Quality Technology 5, pp. 109–122.
   Google Scholar
• Snee, R. D. (1975). “Experimental Designs for Quadratic Models in Constrained Mixture Spaces”. Technometrics 17, pp. 149–159.
   Web of Science ®Google Scholar
• Snee, R. D. (1979). “Experimental Designs for Mixture Systems with Multicomponent Constraints”. Communications in Statistics—Theory and Methods 8, pp. 337–358.
   Web of Science ®Google Scholar
• Snee, R. D. (1981). “Developing Blending Models for Gasoline and Other Mixtures”. Technometrics 23, pp. 229–230.
   Web of Science ®Google Scholar
• Snee, R. D. (1983). “Discussion of ‘Developments in Linear Regression Methodology: 1959–1982’”. Technometrics 25, pp. 230–237.
   Google Scholar
• Snee, R. D. and Marquardt, D. W. (1974). “Extreme Vertices Designs for Linear Mixture Models”. Technometrics 16, pp. 533–537.
   Web of Science ®Google Scholar
• Snee, R. D. and Marquardt, D. W. (1976). “Screening Concepts and Designs for Experiments With Mixtures”. Technometrics 18, pp. 19–29.
   Web of Science ®Google Scholar
• Snee, R. D. and Marquardt, D. W. (1984). “Collinearity Diagnostics Depend on the Domain of Prediction, the Model, and the Data”. The American Statistician 38, pp. 83–87.
   Web of Science ®Google Scholar
• St. John, R. C. and Draper, N. R. (1975). “D-optimality for Regression Designs: A Review”. Technometrics 17, pp. 15–23.
   Web of Science ®Google Scholar
• Steinberg, D. M. and Hunter, W. G. (1984). “Experimental Design: Review and Comment (with Discussion)”. Technometrics 26, pp. 71–130.
   Web of Science ®Google Scholar
• Welch, W. J. (1982). “Branch and Bound Search for Experimental Designs Based on D-optimality and Other Criteria”. Technometrics 24, pp. 41–48.
   Web of Science ®Google Scholar
• Welch, W. J. (1984). “Computer-Aided Design of Experiments for Response Estimation”. Technometrics 26, pp. 217–224.
   Web of Science ®Google Scholar
• Welch, W. J. (1985). “ACED: Algorithms for the Construction of Experimental Designs”. The American Statistician 39, p. 146.
   Google Scholar
• Wheeler, R. E. (1985). “Regression Tool Kit: Software for the Design and Analysis of Regression Experiments, Algorithmic Tool Kit: Software for the Algorithmic Design of Experiments”. The American Statistician 39, p. 144.
   Google Scholar
• Wynn, H. P. (1970). “The Sequential Generation of D-optimum Experimental Designs”. Annals of Mathematical Statistics 41, pp. 1655–1664.
   Google Scholar