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Log-Linear Models

2014
The classical log-linear models are introduced for two-way and multi-way contingency tables. Estimation theory, goodness-of-fit testing, and model selection procedures are discussed. Characteristic examples are worked out in R and interpreted. Log-linear models for three-dimensional tables are illustrated through mosaic plots.
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Aggregation with Log-Linear Models

The Review of Economic Studies, 1992
When economic theory suggests a log-linear specification for individual agents, e.g., CobbDouglas production, it is common to estimate the same log-linear model with aggregate data, invoking a representative agent assumption and thereby assuming away aggregation errors.
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Conditional log-linear structures for log-linear modelling

Computational Statistics & Data Analysis, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Log-Linear Models

2021
Razia Azen, Cindy M. Walker
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Log-Linear Models

2004
In this chapter we study log-linear models which are useful for modeling multivariate discrete data. There is a strong connection between log-linear models and undirected graphs.
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General log-linear modelling

1993
In this chapter the uses of log-linear modelling that have been discussed in Chapter 7 are extended to cover situations where resource selection can be related to factors such as the individual animals involved, or the time of day.
Bryan F. J. Manly   +2 more
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Antibody–drug conjugates: Smart chemotherapy delivery across tumor histologies

Ca-A Cancer Journal for Clinicians, 2022
Paolo Tarantino   +2 more
exaly  

Log-Linear Models: Definition

2018
This chapter introduces log-linear models which are the most widely used simple structures in the analysis of categorical data. Their simplicity comes from a multiplicative structure, where the multipliers depend on subsets of the variables, but not on all variables together.
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Hierarchical Log- Linear Models

2019
Darren George, Paul Mallery
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Log-Linear Models: Interpretation

2018
This chapter starts with the specification and handling of regression type problems for categorical data. The log-linear parameters can be transformed into multiplicative parameters, and these are useful in dealing with the regression problem for categorical variables, where this approach provides a clear and testable concept of separate effects versus
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