Approximate Commutative Algebra pp 193-203
An Introduction to Regression and Errors in Variables from an Algebraic Viewpoint
- First Online:
There is a need to make a closer connection between classical response surface methods and their experimental design aspects, including optimal design, and algebraic statistics, based on computational algebraic geometry of ideals of points. This is a programme which was initiated by Pistone and Wynn (Biometrika, 1996) and is expanding rapidly. Particular attention is paid to the problem of errors in variables which can be taken as a statistical version of the ApCoA research programme.
Unable to display preview. Download preview PDF.
- 7.Fuller, W. A. (1987). Measurement error models. Wiley Series in Probability and Mathematical Statistics: Probability and Mathematical Statistics. Wiley, New York.Google Scholar
- 9.Laubenbacher, R. and Stigler, B. (2008). Design of experiments and biochemical network inference. In Geometric and Algebraic Methods in Statistics (eds. P. Gibilisco, E. Riccomagno, M.P. Rogantin and H.P. Wynn), Cambridge University Press, Cambridge (forthcoming).Google Scholar
- 10.Bühlmann, P. (2008). High-dimensional variable selection: from association to intervention. In Proceedings of the 7th World Congress in Probability and Statistics, Singapore July 14-19, 2008, 80-81.Google Scholar
- 13.Pistone, G., Riccomagno, E. and Wynn, H. P. (2001). Algebraic Statistics. Chapman & Hall/CRC, Boca Raton.Google Scholar
- 15.Ruffa, S. (2004). La statistica algebrica nei modelli di regression con errori nelle variabili (in Italian), Laurea Specialistica, Politecnico di Torino.Google Scholar
- 16.Searle, S. R. (1987). Linear models for unbalanced data, John Wiley Sons.Google Scholar