Label‐ and Level‐Invariant Graphical Log‐Linear Models |
| |
| Authors: | Ricardo Ramírez‐Aldana Guillermina Eslava‐Gómez |
| |
| Affiliation: | Graduate Studies in Mathematics and Department of Mathematics, Faculty of Sciences, UNAM, CU, DF Mexico |
| |
| Abstract: | We introduce two types of graphical log‐linear models: label‐ and level‐invariant models for triangle‐free graphs. These models generalise symmetry concepts in graphical log‐linear models and provide a tool with which to model symmetry in the discrete case. A label‐invariant model is category‐invariant and is preserved after permuting some of the vertices according to transformations that maintain the graph, whereas a level‐invariant model equates expected frequencies according to a given set of permutations. These new models can both be seen as instances of a new type of graphical log‐linear model termed the restricted graphical log‐linear model, or RGLL, in which equality restrictions on subsets of main effects and first‐order interactions are imposed. Their likelihood equations and graphical representation can be obtained from those derived for the RGLL models. |
| |
| Keywords: | category‐invariance graph colourings graph symmetry iterative proportional fitting orbits permutation symmetry |
|
|
