Titlebook: Vector Generalized Linear and Additive Models; With an Implementati Thomas W. Yee Textbook 2015 Thomas Yee 2015 Additive Model Theory.Gener

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Univariate Continuous Distributionslated according to their support, and/or the distribution from which they are derived (e.g., beta-type, gamma-type), and/or their purpose (e.g., statistical size distributions, actuarial distributions).

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Categorical Data Analysisl logit model for nominal responses; and for ordinal responses: the proportional and non-proportional-odds models, continuation and stopping ratio models, and the adjacent categories model. Some other topics includes the .?argument for allowing?..-specific covariates, the Poisson trick, marginal effects, and genetic models.

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Vector Generalized Linear and Additive Models978-1-4939-2818-7Series ISSN 0172-7397 Series E-ISSN 2197-568X

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VGLMsThis chapter covers the most important class of models, viz.?vector generalized linear models (VGLMs). It includes the basic ideas of (parameter) link functions, constraint matrices, the . argument, inference, computational details, (e.g., QR?decomposition, Cholesky), residuals and diagnostics. Some basic details on software usage is given.

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Other TopicsThis chapter looks at a selection of miscellaneous topics, such as calculation of working weights by simulated Fisher scoring (SFS), information criteria for model selection, and bias-reduction (for GLMs). The latter has been used to obtain a finite solution to completely separated binary data.

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Bivariate Continuous DistributionsThis chapter lists a small number of bivariate distributions whose parameters can easily be estimated by IRLS. A handful of a special type of bivariate distribution, called copulas, are also implemented. Some special consideration is given to the bivariate normal distribution and Plackett’s bivariate distribution.

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Thomas W. YeeFirst comprehensive book on Vector Generalized Linear Models.Builds on success of VGAM R package, which is used to show applications of the methodology.Techniques on regression and parametric and non-