Graphical Log-Linear Models: Fundamental Concepts and Applications

Monitoring Multinomial Logit Profiles via Log-Linear Models (Quality Engineering Conference Paper)

In certain statistical process control applications, quality of a process or product can be characterized by a function commonly referred to as profile. Some of the potential applications of profile monitoring are cases where quality characteristic of interest is modelled using binary,multinomial or ordinal variables. In this paper, profiles with multinomial response are studied. For this purpo...

MGraph: graphical models for microarray data analysis

UNLABELLED This paper introduces a MATLAB toolbox, MGraph, which applies graphical models as a natural environment to formulate and solve problems in microarray data analysis. MGraph with its graphical interface allows the user to predict genetic regulatory networks by a graphical gaussian model (GGM), and to quantify the effects of different experimental treatment conditions on gene expression...

Building Measurement Models from Scratch: Graphical Models, Exchangeability Models, and Log-linear Models

Quasi-Symmetric Graphical Log-Linear Models

We propose an extension of graphical log-linear models to allow for symmetry constraints on some interaction parameters that represent homologous factors. The conditional independence structure of such quasi-symmetric (QS) graphical models is described by an undirected graph with coloured edges, in which a particular colour corresponds to a set of equality constraints on a set of parameters. Un...

An Algorithm for Combining Decomposable Graphical Models

We propose an algorithm for combining decomposable graphical models and apply it for building decomposable graphical log-linear models which involve a large number of variables. A main idea in this algorithm is that we group the random variables that are involved in the data into several subsets of variables, build graphical log-linear models for the marginal data, and then combine the marginal...