Results 221 to 230 of about 1,574,453 (263)
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Technometrics, 1992
Throughout the last 15 or 20 years, social scientists have seen a substantial body of literature published in their journals on the subject of analyzing categorical or qualitative data. Many of these articles begin by bemoaning the fact that most of the multivariate statistical tools that social scientists have at their disposal, i.e., the tools that ...
Shiva S. Halli, K. Vaninadha Rao
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Throughout the last 15 or 20 years, social scientists have seen a substantial body of literature published in their journals on the subject of analyzing categorical or qualitative data. Many of these articles begin by bemoaning the fact that most of the multivariate statistical tools that social scientists have at their disposal, i.e., the tools that ...
Shiva S. Halli, K. Vaninadha Rao
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WIREs Computational Statistics, 2011
AbstractThis 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.
Von Eye, Alexander +2 more
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AbstractThis 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.
Von Eye, Alexander +2 more
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1981
The three preceding chapters have all used models in which the response variables were probabilities (Chapters 4 and 5) or a linear combination of probabilities (Chapter 6). In this chapter we consider a model in which the response function involves the natural logarithm of the response variable.
Ron N. Forthofer, Robert G. Lehnen
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The three preceding chapters have all used models in which the response variables were probabilities (Chapters 4 and 5) or a linear combination of probabilities (Chapter 6). In this chapter we consider a model in which the response function involves the natural logarithm of the response variable.
Ron N. Forthofer, Robert G. Lehnen
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2012
This chapter describes graphical models for multivariate discrete (categorical) data. It starts out by describing various different ways in which such data may be represented in R—for example, as contingency tables—and how to convert between these representations.
Søren Højsgaard +2 more
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This chapter describes graphical models for multivariate discrete (categorical) data. It starts out by describing various different ways in which such data may be represented in R—for example, as contingency tables—and how to convert between these representations.
Søren Højsgaard +2 more
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Log-linear modeling using conditional log-linear structures
Annals of the Institute of Statistical Mathematics, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
VELLAISAMY, P, VIJAY, V
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2005
A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The counts are modeled by the Poisson distribution, and related to the classifying variables through a ...
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A large amount of data collected in the social sciences are counts crossclassified into categories. These counts are non-negative integers and require special methods of analysis to model appropriately; log-linear models are one sophisticated method. The counts are modeled by the Poisson distribution, and related to the classifying variables through a ...
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2006
Abstract Log-linear models for multidimensional tables of discrete data were first popularized by Goodman (1970) and Bishop et al. (1975). These models can be interpreted in terms of interactions between the various factors in multidimensional tables and are easily generalized to higher dimensions.
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Abstract Log-linear models for multidimensional tables of discrete data were first popularized by Goodman (1970) and Bishop et al. (1975). These models can be interpreted in terms of interactions between the various factors in multidimensional tables and are easily generalized to higher dimensions.
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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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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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