Showing papers on "Graphical model published in 1984"
TL;DR: In this article, a graphical model diagnosis extending Mandel's approach is developed whereby the response is expressed in terms of "truth-connected" latent variates and, ultimately, by originally measured external variates.
Abstract: Graphical model diagnosis procedures for two-way tables have already been proposed by Mandel, as well as by Bradu and Gabriel who used Gabriel's biplot as a main tool. In this paper, a graphical model diagnosis extending Mandel's approach is developed. Response surface models are obtained whereby the response is expressed in terms of ‘truth-connected’ latent variates and, ultimately, in terms of originally measured external variates. Meaningful models are obtained in some cases, accurate smoothing and interpolation algorithms in others. As a by–product, Euclidean maps, which represent a twodimensional scaling (for rows or columns) also displaying ,ANOVA features, are obtained. To an extent, these maps can be viewed as a substitute for a model in the event of partial failure of the modelling operation.
8 citations
01 Jan 1984
TL;DR: Fitting heirarchical loglinear models to contingency tables classified by k response factors is simplified, both theoretically and computationally by restricting attention to conditional independence (graphical) models.
Abstract: Fitting heirarchical loglinear models to contingency tables classified by k response factors is simplified, both theoretically and computationally by restricting attention to conditional independence (graphical) models. In an exploratory study there is a need to assess all such models. A procedure is outlined here by which most, though not quite all, of the relevant information can be rapidly computed and processed. This procedure is illustrated with a five way table.
6 citations
01 Jan 1984
TL;DR: In this article, the system consists of two relatively independent parts: the general linear model (GLM) which can be especially used for processing of continuous type of data and the general log-linear model (GLLM), which is useful for processing categorical data.
Abstract: The system consists of two relatively independent parts. The first is the general linear model (GLM) which can be especially used for processing of continuous type of data. The second is the general log-linear model (GLLM) and is useful for processing of categorical data.