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URN	etd-0629105-082255
Author	Wei-shan Hsieh
Author's Email Address	b8824054@student.nsysu.edu.tw
Statistics	This thesis had been viewed 5064 times. Download 1340 times.
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	etd-0629105-082255.pdf indicate accessible in a year
Date of Submission	2005-06-29

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