URN |
etd-0629105-082255 |
Author |
Wei-shan Hsieh |
Author's Email Address |
b8824054@student.nsysu.edu.tw |
Statistics |
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Department |
Applied Mathematics |
Year |
2004 |
Semester |
2 |
Degree |
Master |
Type of Document |
|
Language |
English |
Title |
Optimum Designs for Model Discrimination and Estimation in Binary Response Models |
Date of Defense |
2005-06-03 |
Page Count |
36 |
Keyword |
Least square estimate
model robustness
model discrimination
mean square error
symmetric location and scale family
|
Abstract |
This paper is concerned with the problem of finding an experimental design for discrimination between two rival models and for model robustness that minimizing the maximum bias simultaneously in binary response experiments. The criterion for model discrimination is based on the $T$-optimality criterion proposed in Atkinson and Fedorov (1975), which maximizes the sum of squares of deviations between the two rival models while the criterion for model robustness is based on minimizing the maximum probability bias of the two rival models. In this paper we obtain the optimum designs satisfy the above two criteria for some commonly used rival models in binary response experiments such as the probit and logit models etc. |
Advisory Committee |
Fu-Chuen Chang - chair
Mei-Hui Guo - co-chair
Ray-Bing Chen - co-chair
Kam-Fai Wong - co-chair
Mong-Na Lo Huang - advisor
|
Files |
indicate accessible in a year |
Date of Submission |
2005-06-29 |