A class of statistical models to weaken independence in two-way contingency tables
Statistics Theory
2008-04-29 v2 Algebraic Geometry
Methodology
Statistics Theory
Abstract
In this paper we study a new class of statistical models for contingency tables. We define this class of models through a subset of the binomial equations of the classical independence model. We use some notions from Algebraic Statistics to compute their sufficient statistic, and to prove that they are log-linear. Moreover, we show how to compute maximum likelihood estimates and to perform exact inference through the Diaconis-Sturmfels algorithm. Examples show that these models can be useful in a wide range of applications.
Keywords
Cite
@article{arxiv.0803.1582,
title = {A class of statistical models to weaken independence in two-way contingency tables},
author = {Enrico Carlini and Fabio Rapallo},
journal= {arXiv preprint arXiv:0803.1582},
year = {2008}
}
Comments
A theorem has been removed because of a gap in the proof. Minor style changes