Abstract
This article describes log-linear models as special cases of generalized linear models. Specifically, log-linear models use a logarithmic link function. Log-linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns. Three types of log-linear models are discussed, hierarchical models, nonhierarchical models, and nonstandard models. Emphasis is placed on parameter interpretation. It is demonstrated that parameters are best interpretable when they represent the effects specified in the design matrix of the model. Parameter interpretation is illustrated first for a standard hierarchical model, and then for a nonstandard model that includes structural zeros. In a data example, the relationships among race of defendant, race of victim, and death penalty sentence are examined using a log-linear model with all three two-way interactions. Recent developments in log-linear modeling are discussed.
| Original language | English |
|---|---|
| Pages (from-to) | 218-223 |
| Number of pages | 6 |
| Journal | Wiley Interdisciplinary Reviews: Computational Statistics |
| Volume | 4 |
| Issue number | 2 |
| DOIs | |
| State | Published - 1 Mar 2012 |
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Keywords
- Hierarchical models
- Log-linear modeling
- Nonhierarchical models
- Nonstandard models
- Parameter interpretation
Cite this
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Log-linear modeling. / von Eye, Alexander; Mun, Eun-Young; Mair, Patrick.
In: Wiley Interdisciplinary Reviews: Computational Statistics, Vol. 4, No. 2, 01.03.2012, p. 218-223.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Log-linear modeling
AU - von Eye, Alexander
AU - Mun, Eun-Young
AU - Mair, Patrick
PY - 2012/3/1
Y1 - 2012/3/1
N2 - This article describes log-linear models as special cases of generalized linear models. Specifically, log-linear models use a logarithmic link function. Log-linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns. Three types of log-linear models are discussed, hierarchical models, nonhierarchical models, and nonstandard models. Emphasis is placed on parameter interpretation. It is demonstrated that parameters are best interpretable when they represent the effects specified in the design matrix of the model. Parameter interpretation is illustrated first for a standard hierarchical model, and then for a nonstandard model that includes structural zeros. In a data example, the relationships among race of defendant, race of victim, and death penalty sentence are examined using a log-linear model with all three two-way interactions. Recent developments in log-linear modeling are discussed.
AB - This article describes log-linear models as special cases of generalized linear models. Specifically, log-linear models use a logarithmic link function. Log-linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns. Three types of log-linear models are discussed, hierarchical models, nonhierarchical models, and nonstandard models. Emphasis is placed on parameter interpretation. It is demonstrated that parameters are best interpretable when they represent the effects specified in the design matrix of the model. Parameter interpretation is illustrated first for a standard hierarchical model, and then for a nonstandard model that includes structural zeros. In a data example, the relationships among race of defendant, race of victim, and death penalty sentence are examined using a log-linear model with all three two-way interactions. Recent developments in log-linear modeling are discussed.
KW - Hierarchical models
KW - Log-linear modeling
KW - Nonhierarchical models
KW - Nonstandard models
KW - Parameter interpretation
UR - http://www.scopus.com/inward/record.url?scp=84856703842&partnerID=8YFLogxK
U2 - 10.1002/wics.203
DO - 10.1002/wics.203
M3 - Article
VL - 4
SP - 218
EP - 223
JO - Wiley Interdisciplinary Reviews: Computational Statistics
JF - Wiley Interdisciplinary Reviews: Computational Statistics
SN - 1939-5108
IS - 2
ER -