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Generalized Linear Models (GLM)
2021Abstract Chapter 7 introduces one of the most useful statistical frameworks for the modern life scientist: the generalized linear model (GLM). GLMs extend the linear model to an array of non-normally distributed data such as Poisson, negative binomial, binomial, and Gamma distributed data. These models dramatically improve the breadth of
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2015
For completeness, this chapter summarizes some relevant aspects of the linear model (LM) and generalized linear model (GLM) for the book. A basic understanding of these is helpful when considering VGLMs later. Some topics covered include link functions, the exponential family, assumptions, estimation (especially IRLS), numerical and computing aspects ...
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For completeness, this chapter summarizes some relevant aspects of the linear model (LM) and generalized linear model (GLM) for the book. A basic understanding of these is helpful when considering VGLMs later. Some topics covered include link functions, the exponential family, assumptions, estimation (especially IRLS), numerical and computing aspects ...
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Proposed for presentation at the GLM Science Meeting held September 13-15, 2022 in Huntsville, Al., 2022
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Variantieanalyse: De GLM-benadering
2002Item does not contain ...
Bercken, J.H.L. van den, Voeten, M.J.M.
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Verallgemeinertes Lineares Modell (GLM)
1996Die beiden grosen Methodenfamilien innerhalb der linearen Modelle, die Varianz- und die Regressionsanalyse, gehen beide von der Voraussetzung aus, das sich die Kriteriumsvariable additiv aus einer Erwartungswertfunktion und einer Fehlervariablen zusammensetzt, das (1) die Fehlervariable normalverteilt ist (mit konstanter Varianz) und das
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1994
Abstract Generalized linear models (GLM) are an extension of the linear regression models beyond the realm of the normal distribution. Their unified formulation, as opposed to a set of distinct methods for different distributional assumptions, is due to Nelder and Wedderburn (1972) and Wedderburn (1974).
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Abstract Generalized linear models (GLM) are an extension of the linear regression models beyond the realm of the normal distribution. Their unified formulation, as opposed to a set of distinct methods for different distributional assumptions, is due to Nelder and Wedderburn (1972) and Wedderburn (1974).
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