Results 141 to 150 of about 768,731 (196)
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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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Aggregation with Log-Linear Models
The Review of Economic Studies, 1992When 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, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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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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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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
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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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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