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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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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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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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Equivalence of Generative and Log-Linear Models
IEEE Transactions on Audio, Speech, and Language Processing, 2011Conventional speech recognition systems are based on hidden Markov models (HMMs) with Gaussian mixture models (GHMMs). Discriminative log-linear models are an alternative modeling approach and have been investigated recently in speech recognition. GHMMs are directed models with constraints, e.g., positivity of variances and normalization of conditional
Georg Heigold +4 more
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Table selection and log-linear models
Journal of Chronic Diseases, 1980Abstract The use of multi-dimensional contingency tables has become commonplace in the analysis of epidemiological data. This paper examines two problems in this methodology. First, for data having many response or dependent variables, it is often unclear as to which of many possible tables should be analyzed.
D H, Freeman, J F, Jekel
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Log-Linear Modelling and Spatial Analysis
Environment and Planning A: Economy and Space, 1985In the past decade the social sciences have seen an upsurge of interest in analysing multidimensional contingency tables using log-linear models. Two broad families of log-linear models may be distinguished: the family of conventional models and the family of unconventional models (that is, quasi-log-linear and hybrid models).
E Aufhauser, M M Fischer
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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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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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