Discrimination Between Random and Fixed Effect Logistic Regression Models
- Tommasi, Chiara 2
- Santos-Martín, Maria Teresa 1
- Rodríguez-Díaz, Juan Manuel 1
- 1 Department of Statistics, University of Salamanca, Spain
- 2 Department of Economics, Business and Statistics, University of Milan, Italy
- Jesús López-Fidalgo (ed. lit.)
- Juan Manuel Rodríguez-Díaz (ed. lit.)
- Ben Torsney (ed. lit.)
ISSN: 1431-1968
ISBN: 978-3-7908-2409-4, 978-3-7908-2410-0
Année de publication: 2010
Pages: 205-212
Type: Chapitre d'ouvrage
Références bibliographiques
- Abdelbasit, K. and R. Plackett (1983). Experimental design for binary data. Journal of the American Statistical Association 78, 90–98.
- Demidenko, E. (2004). Mixed Models: Theory and Applications. New York: Wiley.
- Dette, H. and S. Titoff (2009). Optimal discrimination designs. Annals of Statistics 37, 2056–2082.
- Graßhoff, U., H. Holling, and R. Schwabe (2009). On optimal design for a heteroscedastic model arising from random coefficients.In Proceedings of the 6th St. Petersburg Workshop on Simulation, St. Petersburg, pp. 387–392. VVM com. Ltd.
- López-Fidalgo, J., C. Trandafir, and C. Tommasi (2007). An optimal experimental design criterion for discriminating between non-normal models. Journal of the Royal Statistical Society, Series B 69, 231–242.
- Mentré, F., A. Mallet, and D. Baccar (1997). Optimal design in random-effects regression models. Biometrika 84, 429–442.
- Minkin, S. (1987). Optimal designs for binary data. Journal of the American Statistical Association 82, 1098–1103.
- Ouwens, M., T. Frans, and B. Martijn (2006). A maximin criterion for the logistic random intercept model with covariates. Journal of Statistical Planning and Inference 136, 962–981.
- Patan, M. and B. Bogacka (2007). Efficient sampling windows for parameter estimation in mixed effects models. In mODa 8 - Advances in Model-Oriented Design and Analysis, New York, pp. 147–155. Physica-Verlag.
- Silvey, S. (1980). Optimal Design. New York: Chapman & Hall.
- Sitter, R. and I. Fainaru (1997). Optimal designs for the logit and probit models for binary data. Canadian Journal of Statistics 25, 175–190.