Efficiencies for optimum designs when transforming the response in nonlinear models with nonconstant variance

Metrika

Volumn 62, Issue 2-3, 2005, Pages 127-138

  Atkinson, Anthony C ^a  

^a LONDON SCHOOL OF ECONOMICS   (United Kingdom)

Author keywords

Box Cox transformation; Chemical kinetics; D optimum design; Design efficiency; Mechanistic model; Parameter sensitivities; Robust designs; Transform both sides

Indexed keywords

EID: 26444456920     PISSN: 00261335     EISSN: None     Source Type: Journal    
DOI: 10.1007/s00184-005-0406-z     Document Type: Conference Paper

References (17)

1. Atkinson, A.C.: Horwitz's rule, transforming both sides and the design of experiments for mechanistic models. Appl. Stat. 52, 261-278 (2003)
2. Atkinson, A.C.: Some Bayesian optimum designs for response transformation in nonlinear models with nonconstant variance. In: A. Di Bucchianico, H. Läuter, H.P. Wynn (eds.), MODA 7 - Advances in Model-Oriented Design and Analysis, Heidelberg: Physica-Verlag, 2004a, pp. 13-22
3. Atkinson, A.C.: Transforming both sides and optimum experimental design for a nonlinear model arising from second-order chemical kinetics. Tatra Mountains Mathematical Publications 26, 29-39 (2004b)
4. Atkinson, A.C., Cook, R.D.: Designing for a response transformation parameter. J. R. Stat. Soc. B 59, 111-124 (1997)
5. Atkinson, A.C., Riani, M.: Robust Diagnostic Regression Analysis. New York: Springer-Verlag, 2000
6. Bauer, I., Bock, H.G., Körkel, S., Schlöder, J.P.: Numerical methods for optimum experimental design in DAE systems. J. Comput. Appl. Math. 120, 1-25 (2000)
7. Bogacka, B., Wright, F.: A non-linear design problem in a chemical kinetic model with non-constant error variance. J. Stat. Planning and Inference 128, 633-648 (2005)
8. Box, G.E.P., Cox, D.R.: An analysis of transformations (with discussion). J. R. Stat. Soc. Series B 26, 211-246 (1964)
9. Box, G.E.P., Lucas, H.L.: Design of experiments in nonlinear situations. Biometrika 46, 77-90 (1959)
10. Carroll, R.J., Ruppert, D.: Transformation and Weighting in Regression. London: Chapman and Hall, 1988
11. Downing, D., Fedorov, V.V., Leonov, S. (2001). Extracting information from the variance function: optimal design. In: A.C. Atkinson, P. Hackl, W.G. Müller (eds.), MODA 6 - Advances in Model-Oriented Design and Analysis, Heidelberg: Physica-Verlag, pp. 45-52
12. Kounias, S., Koukouvinos, C., Nikolaou, N., Kakos, A.: The non-equivalent circulant D-optimal designs for n = 90. J. Stat. Planning and Inference 53, 253-259 (1996)
13. Li, W., Nachtsheim, C.J.: Model-robust factorial designs. Technometrics 42, 345-352 (2000)
14. Lindsey, J.K.: Nonlinear Models in Medical Statistics. Oxford: Oxford University Press, 2001
15. Lischer, P.: Good statistical practice in analytical chemistry. In: B. Grigelionis (ed.), Probability Theory and Mathematical Statistics, Dordrecht: VSP, 1999, pp. 1-12
16. Müller, W.G., Pázman, A.: Measures for designs in experiments with correlated errors. Biometrika 90, 423-434 (2003)
17. Uciński, D., Atkinson, A.C.: Experimental design for processes over time. Studies in Nonlinear Dynamics and Econometrics 2(2). (Article 13) (2004)