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Introduction.- Loglinear marginal models.- Nonloglinear marginal models.- Marginal analysis of longitudinal data.- Causal analysis: structural equation models and (quasi-)experimental designs.- Marginal modeling with latent variables.- Conclusions, extensions, applications.
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Add Marginal Models, Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answ, Marginal Models to the inventory that you are selling on WonderClubX
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Add Marginal Models, Marginal Models for Dependent, Clustered, and Longitudinal Categorical Data provides a comprehensive overview of the basic principles of marginal modeling and offers a wide range of possible applications. Marginal models are often the best choice for answ, Marginal Models to your collection on WonderClub |