Some robust design strategies for percentile estimation in binary response models

Canadian Journal of Statistics

Volumn 34, Issue 4, 2006, Pages 603-622

  Biedermann, Stefanie ^a   Dette, Holger ^b   Pepelyshev, Andrey ^c  

^a UNIVERSITY OF SOUTHAMPTON   (United Kingdom)

^b RUHR UNIVERSITY BOCHUM   (Germany)

^c ST PETERSBURG STATE UNIVERSITY   (Russian Federation)

Author keywords

Binary response model; C efficiency; Multiobjective design; Optimal design; Percentile estimation; Robustness

Indexed keywords

EID: 33847753823     PISSN: 03195724     EISSN: None     Source Type: Journal    
DOI: 10.1002/cjs.5550340404     Document Type: Article

References (23)

1. K. Chaloner & K. Larntz (1989). Optimal Bayesian experimental design applied to logistic regression. Journal of Statistical Planning and Inference, 21, 191-208.
2. H. Chernoff (1953). Locally optimal designs for estimating parameters. The Annals of Mathematical Statistics, 24, 586-602.
3. H. Dette (1997). Designing experiments with respect to standardized optimality criteria. Journal of the Royal Statistical Society Series B, 59, No. 1, 97-110.
4. H. Dette & S. Biedermann (2003). Robust and efficient designs for the Michaelis-Menten model. Journal of the American Statistical Association, 98, 679-686.
5. H. Dette, L. Haines & L. Imhof (2006). Maximin and Bayesian optimal designs for linear and nonlinear regression models. Statistica Sinica, in press.
6. H. Dette & W. K. Wong (1996). Optimal Bayesian designs for models with partially specified heteroscedastic structure. The Annals of Statistics, 24, 2108-2127.
7. R. Fandom Noubiap & W. Seidel (2000). A minimax algorithm for constructing optimal symmetrical balanced designs for a logistic regression model. Journal of Statistical Planning and Inference, 91, 151-168.
8. Z. Fang, D. P. Wiens & Z. Wu (2006). Locally D-optimal designs for multistage models and heteroscedastic polynomial regression models. Journal of Statistical Planning and Inference, 136, 4059-4070.
9. I. Ford, B. Torsney & C. F. J. Wu (1992). The use of a canonical form in the construction of locally optimal designs for non-linear problems. Journal of the Royal Statistical Society Series B, 54, 569-583.
10. J. King & W. K. Wong (2000). Minimax D-optimal designs for the logistic model. Biometrics, 56, 1263-1267.
11. E. Läuter (1974). Experimental design in a class of models. Mathematische Operationsforschung und Statistik, 5, 379-398.
12. J. López-Fidalgo & W. K. Wong (2000). A comparative study of MV- and SMV-optimal designs for binary response models. In Advances in Stochastic Simulation Methods (N. Balakrishnan, ed.), Birkhäuser, Boston, pp. 135-151.
13. C. H. Müller (1995). Maximin efficient designs for estimating nonlinear aspects in linear models. Journal of Statistical Planning and Inference, 44, 117-132.
14. F. Pukelsheim (1993). Optimal Design of Experiments. Wiley, New York.
15. W. F. Rosenberger & S. E. Grill (1997). A sequential design for psychophysical experiments: an application to estimating timing of sensory events. Statistics in Medicine, 16, 2245-2260.
16. S. D. Silvey (1980). Optimal Design. Chapman & Hall, London.
17. R. R. Sitter (1992). Robust designs for binary data. Biometrics, 48, 1145-1155.
18. R. R. Sitter & C. F. J. Wu (1993). Optimal designs for binary response experiments: Fieller-, D- and A-criteria. Scandinavian Journal of Statistics, 20, 329-341.
19. C. F. J. Wu (1985). Efficient sequential designs with binary data. Journal of the American Statistical Association, 80, 974-984.
20. C. F. J. Wu (1988). Optimal design for percentile estimation of a quantal response curve. In Optimal Design and Analysis of Experiments (Y. Dodge, V. V. Fedorov & H. P. Wynn, eds.), Elsevier/North Holland, Amsterdam, pp. 213-223.
21. W. Zhu, H. Ann & W. K. Wong (1998). Multiple-objective optimal designs for the logistic model. Communications in Statistics: Theory and Methods, 27, 1581-1592.
22. W. Zhu & W. K. Wong (2000). Multiple-objective designs in a dose-response experiment. Journal of Biopharmaceutical Statistics, 10, No. 1, 1-14.
23. W. Zhu & W. K. Wong (2001). Bayesian optimal designs for estimating a set of symmetric quantiles. Statistics in Medicine, 20, 123-137.