| Type of Document |
Dissertation |
| Author |
Pickle, Stephanie M
|
| URN |
etd-08042006-075722 |
| Title |
Semiparametric Techniques for Response Surface Methodology |
| Degree |
PhD |
| Department |
Statistics |
| Advisory Committee |
| Advisor Name |
Title |
| Birch, Jeffrey B. |
Committee Chair |
| Robinson, Timothy J. |
Committee Co-Chair |
| Prins, Samantha C. Bates |
Committee Member |
| Spitzner, Dan J. |
Committee Member |
| Vining, G. Geoffrey |
Committee Member |
|
| Keywords |
- Genetic Algorithm
- Response Surface Methodology
- Semiparametric Regression
- Robust Parameter Design
|
| Date of Defense |
2006-06-28 |
| Availability |
unrestricted |
Abstract
Many industrial statisticians employ the techniques of Response Surface Methodology (RSM) to study and optimize products and processes. A second-order Taylor series approximation is commonly utilized to model the data; however, parametric models are not always adequate. In these situations, any degree of model misspecification may result in serious bias of the estimated response. Nonparametric methods have been suggested as an alternative as they can capture structure in the data that a misspecified parametric model cannot. Yet nonparametric fits may be highly variable especially in small sample settings which are common in RSM. Therefore, semiparametric regression techniques are proposed for use in the RSM setting. These methods will be applied to an elementary RSM problem as well as the robust parameter design problem.
|
| Files |
| Filename |
Size |
Approximate Download Time
(Hours:Minutes:Seconds)
|
| 28.8 Modem |
56K Modem |
ISDN (64 Kb) |
ISDN (128 Kb) |
Higher-speed Access |
| |
SMPickle_Dissertation.pdf |
657.34 Kb |
00:03:02 |
00:01:33 |
00:01:22 |
00:00:41 |
00:00:03 |
|
