Browse > Article

A method of selecting an active factor and its robustness against correlation in the data  

Yamada, Shu (Department of Management Science Tokyo University of Science Kagurazaka)
Harashima, Jun (Department of Production, Information and Systems Engineering Tokyo Metropolitan Institute of Technology)
Publication Information
International Journal of Quality Innovation / v.4, no.2, 2003 , pp. 16-31 More about this Journal
Abstract
A reducing variation of quality characteristics is a typical example of quality improvement. In such a case, we treat the quality characteristic, as a response variable and need to find active factors affecting the response from many candidate factors since reducing the variation of the response will be achieved by reducing variation of the active factors. In this paper, we first derive a method of selecting an active factor by linear regression. It is well known that correlation between factors deteriorates the precision of estimators. We, therefore, examine robustness of the selecting method against the correlation in the data set and derive an evaluation method of the deterioration brought by the correlation. Furthermore, some examples of selecting and evaluation methods are shown to demonstrate practical usage of the methods.
Keywords
Correct selection; Deterioration by correlation; Simulation study; Variable selection;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Myers, R. H., (1990), Classical and Modern Regression with Applications, 2nd ed., Boston: PWS-Kent, p.363
2 Montgomery, D. C. and Peck, E. A., (1992), Introduction to Linear Regression Analysis, 2nd ed., New York: John Wiley, pp. 305-365
3 Lin, D. K. J., (1993b), Another Look at First-Order Saturated Designs: The p-efficient Designs, Technometrics, 35, pp.284-292   DOI   ScienceOn
4 Lin, D. K. J., (1993a), A New Class of Supersaturated Designs, Technometrics, 35, pp.28-31   DOI   ScienceOn
5 Yamada, S., Effects of Correlation Between Explanatory Variables in the Linear Calibration Problem, Quality, 27, pp.117-124. (in Japanese)
6 Draper, N. R. and Smith, H., (1982), Applied Regression Analysis, 2nd ed., New York: John Wiley, p.404
7 Plackett, R. L. and Burman, J. P., (1946), The Design of Optimum Multifactorial Experiments, Biometrika, 33, pp.303-325
8 Box, G. E. P. and Draper, N. R., (1987), Empirical Model-Building and Response Surfaces, New York: John Wiley, pp. 10-14