A Bayesian A-Optimal and Model Robust Design Criterion

Biometrics

Volumn 59, Issue 4, 2003, Pages 1082-1088

  Zhou, Xiaojie ^a   Joseph, Lawrence ^b,c   Wolfson, David B ^d   Bélisle, Patrick ^b  

^a PROCTER AND GAMBLE COMPANY   (United States)

^b MONTREAL GENERAL HOSPITAL   (Canada)

^c MCGILL UNIVERSITY   (Canada)

^d MCGILL UNIVERSITY   (Canada)

Author keywords

A optimality; Bayesian optimal design; Decision theory; Model robustness

Indexed keywords

OPTIMAL SYSTEMS; UNCERTAINTY ANALYSIS; WATER POLLUTION;

A-OPTIMALITY; BAYESIAN; BAYESIAN OPTIMAL DESIGNS; CANDIDATE MODELS; DESIGN CRITERIA; FINITE SET; MODEL ROBUSTNESS; OPTIMAL DESIGN; PARAMETERS ESTIMATION; ROBUST DESIGNS;

DECISION THEORY;

BAYESIAN ANALYSIS; MODEL; UNCERTAINTY ANALYSIS;

ANALYTIC METHOD; ARTICLE; BAYES THEOREM; CHROMATOGRAPHY; DECISION THEORY; MEASUREMENT; MODEL; POLLUTION; SAFETY; SPECTROMETRY; WATER CONTAMINATION;

EID: 0347994078     PISSN: 0006341X     EISSN: None     Source Type: Journal    
DOI: 10.1111/j.0006-341X.2003.00124.x     Document Type: Article

References (16)

1. Box, G. E. P. and Draper, N. R. (1959). A basis for the selection of a response surface design. Journal of the American Statistical Association 54, 622-654.
2. Cook, R. D. and Nachtsheim, C. J. (1982). Model-robust, linear optimal designs. Technometrics 24, 49-54.
3. Currie, L. (1985). The many dimensions of detection in chemical analysis with special emphasis on the one-dimensional calibration curve. In Trace Residue Analysis, Kurtz, D. (ed), 49-81. Washington, D.C.: American Chemical Society.
4. Dette, H. (1993). Bayesian D-optimal and model robust designs in linear regression models. Statistics 25, 27-46.
5. Dette, H. and Studden, W. J. (1995). Optimal designs for polynomial regression when the degree is not known. Statistica Sinica 5, 459-473.
6. Hardy, G., Littlewood, J. E., and Polya, G., (1997). Inequalities, 2nd edition. Cambridge: Cambridge University Press.
7. Hill, P. D. H. (1978). A review of experimental design procedures for regression model discrimination. Technometrics 9, 57-71.
8. Key, J., Pericchi, L., and Smith A. F. M. (1999). Bayesian model choice: What and why? (with discussion). In Bayesian Statistics 5, J. Bernardo et al. (eds), 343-370. Oxford: University Press.
9. Kurtz, D. (1985). Trace Residue Analysis. Washington, D.C.: American Chemical Society.
10. Kurtz, D., Rosenberger, J., and Tamayo, G. (1985). The linear calibration graph and its confidence bands from regression on transformed data. In Trace Residue Analysis, D. Kurtz (ed), 133-165. Washington, D.C.: American Chemical Society.
11. Läuter, E. (1974). Experimental design in a class of models. Mathematische Operationsforschung und Statistik 5, 379-396.
12. Läuter, E. (1976). Optimal multipurpose designs for regression models. Mathematische Operationsforschung und Statistik 7, 51-68.
13. Mitchell, D. (1985). Calibration-based-curve analysis: Use of multiple curve and weighted least squares procedures with confidence band statistics. In Trace Residue Analysis, D. Kurtz (ed), 115-131. Washington, D.C.: American Chemical Society.
14. Raftery, A., Madigan, D., and Hoeting J. (1997). Bayesian model averaging for linear regression models. Journal of the American Statistical Association 92, 179-191.
15. Silvey, S. D. (1980). Optimal Design. London: Chapman and Hall.
16. Spiegelhalter, D. J., Thomas, A., Best, N., and Gilks, W. (1997). BUGS 0.6 - Bayesian Inference Using Gibbs Sampling. Cambridge, U.K.: MRC Biostatistics Unit.