Maruri-Aguilar, Hugo, Saenz-de-Cabezon, Eduardo and Wynn, Henry P.
(2012)
*Betti numbers of polynomial hierarchical models for experimental designs.*
Annals of Mathematics and Artificial Intelligence, 64 (4).
pp. 411-426.
ISSN 1012-2443

## Abstract

Polynomial models, in statistics, interpolation and other fields, relate an output η to a set of input variables (factors), x = (x 1,..., x d), via a polynomial η(x 1,..., x d). The monomials terms in η(x) are sometimes referred to as "main effect" terms such as x 1, x 2, ..., or "interactions" such as x 1x 2, x 1x 3, ... Two theories are related in this paper. First, when the models are hierarchical, in a well-defined sense, there is an associated monomial ideal generated by monomials not in the model. Second, the so-called "algebraic method in experimental design" generates hierarchical models which are identifiable when observations are interpolated with η(x) based at a finite set of points: the design. We study conditions under which ideals associated with hierarchical polynomial models have maximal Betti numbers in the sense of Bigatti (Commun Algebra 21(7):2317-2334, 1993). This can be achieved for certain models which also have minimal average degree in the design theory, namely "corner cut models".

Item Type: | Article |
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Official URL: | http://www.springer.com/computer/ai/journal/10472 |

Additional Information: | © 2012 Springer |

Divisions: | LSE |

Subjects: | H Social Sciences > HA Statistics Q Science > QA Mathematics |

Sets: | Research centres and groups > Decision Support and Risk Group (DSRG) |

Date Deposited: | 23 May 2012 11:06 |

Last Modified: | 20 Feb 2019 10:07 |

URI: | http://eprints.lse.ac.uk/id/eprint/43870 |

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