Advertisement

Advanced Regression and Alternatives

  • Theodore T. Allen
Chapter

Abstract

Linear regression models are not the only curve-fitting methods in wide use. Also, these methods are not useful for analyzing data for categorical responses. In this chapter, so-called “kriging” models, “artificial neural nets” (ANNs), and logistic regression methods are briefly described. ANNs and logistic regression methods are relevant for categorical responses. Each of the modeling methods described here offers advantages in specific contexts. However, all of these alternatives have a practical disadvantage in that formal optimization must be used in their fitting process.

References

  1. Allen T, Bernshteyn M, Kabiri K, Yu L (2003) A comparison of alternative methods for constructing meta-models for computer experiments. J Qual Technol 35(2):1–17Google Scholar
  2. Ben-Akiva M, Steven RL (1985) Discrete choice analysis. MIT Press, Cambridge, MassGoogle Scholar
  3. Chambers M (2000) Queuing network construction using artificial neural networks. PhD dissertation, The Ohio State University, ColumbusGoogle Scholar
  4. Cybenko G (1989) Approximations by superpositions of a sigmoidal function. Mathematics of control, signals, and systems. Springer, New YorkGoogle Scholar
  5. Hadj-Alouane AB, Bean JC (1997) A genetic algorithm for the multiple-choice integer program. Oper Res 45:92–101MathSciNetCrossRefGoogle Scholar
  6. Hosmer DW, Lemeshow S (1989) Applied logistic regression. Wiley, New YorkzbMATHGoogle Scholar
  7. Kohonen T (1989) Self-organization and associative memory. Springer series in information sciences, vol 8, 3rd edn. Springer, LondonGoogle Scholar
  8. Matheron G (1963) Principles of geostatistics. Econ Geol 58:1246–1266CrossRefGoogle Scholar
  9. McKay MD, Conover WJ, Beckman RJ (1979) A comparison of three methods for selection values of input variables in the analysis of output from a computer code. Technometrics 21:239–245MathSciNetzbMATHGoogle Scholar
  10. Reed RD, Marks RJ (1999) Neural smithing: supervised learning and feed-forward artificial neural net. MIT Press, Cambridge, MassGoogle Scholar
  11. Ribardo C (2000) Desirability functions for comparing parameter optimization methods and for addressing process variability under six sigma assumptions. PhD dissertation, Industrial & Systems Engineering, The Ohio State University, ColumbusGoogle Scholar
  12. Rumelhart DE, McClelland JL (eds) (1986) Parallel distributed processing: exploration in the microstructure of cognition. Foundations, vol 1. MIT Press, Cambridge, MassGoogle Scholar
  13. Sacks J, Welch W, Mitchell T, Wynn H (1989) Design and analysis of computer experiment. Stat Sci 4:409–435Google Scholar
  14. Sandor Z, Wedel M (2001) Designing conjoint choice experiments using managers’ prior beliefs. J Mark Res XXXVIII:430–444CrossRefGoogle Scholar
  15. Welch WJ, Buck, RJ, Sacks J, Wynn HP, Mitchell TJ, Morris MD (1992) Screening, predicting, and computer experiments. Technometrics 34:15–25CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Industrial and Systems EngineeringThe Ohio State UniversityColumbusUSA

Personalised recommendations