Analysis of Categorical Data Under Log-Linear Models

2016 ◽  
pp. 135-156
Author(s):  
Parimal Mukhopadhyay
Keyword(s):  
Categorical Data ◽  
Linear Models ◽  
Log Linear

Using log-linear models to analyse categorical data

2001 ◽  
Vol 6 (5) ◽  
pp. 867-875
Author(s):  
Phil McEvoy ◽  
David Richards
Keyword(s):  
Categorical Data ◽  
Linear Models ◽  
Log Linear

scholarly journals A Framework to Interpret Nonstandard Log-Linear Models

2016 ◽  
Vol 36 (2) ◽  
Author(s):  
Patrick Mair
Keyword(s):  
Generalized Linear Models ◽  
Categorical Data ◽  
Linear Models ◽  
Contingency Tables ◽  
Design Matrix ◽  
Nested Models ◽  
Testing Strategies ◽  
The Family ◽  
Parsimonious Models ◽  
Log Linear

The formulation of log-linear models within the framework of Generalized Linear Models offers new possibilities in modeling categorical data. The resulting models are not restricted to the analysis of contingency tables in terms of ordinary hierarchical interactions. Such models are considered as the family of nonstandard log-linear models. The problem that can arise is an ambiguous interpretation of parameters. In the current paperthis problem is solved by looking at the effects coded in the design matrix and determining the numerical contribution of single effects. Based on these results, stepwise approaches are proposed in order to achieve parsimonious models. In addition, some testing strategies are presented to test such (eventually non-nested) models against each other. As a result, a whole interpretation framework is elaborated to examine nonstandard log-linear models in depth.

scholarly journals Log linear models for ordinal multidimensional categorical data

2007 ◽  
Vol 35 (1) ◽  
pp. 29 ◽  
Author(s):  
Prabhani Kuruppumullage ◽  
Roshini Sooriyarachchi
Keyword(s):  
Categorical Data ◽  
Linear Models ◽  
Log Linear
Sign in / Sign up

Export Citation Format

Share Document