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- Generalized_additive_model abstract "In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.GAMs were originally developed by Trevor Hastie and Robert Tibshirani to blend properties of generalized linear models with additive models.The model relates a univariate response variable, Y, to some predictor variables, xi. An exponential family distribution is specified for Y (for example normal, binomial or Poisson distributions) along with a link function g (for example the identity or log functions) relating the expected value of Y to the predictor variables via a structure such as The functions fi(xi) may be functions with a specified parametric form (for example a polynomial, or a coefficient depending on the levels of a factor variable) or maybe specified non-parametrically, or semi-parametrically, simply as 'smooth functions', to be estimated by non-parametric means. So a typical GAM might use a scatterplot smoothing function, such as a locally weighted mean, for f1(x1), and then use a factor model for f2(x2). This flexibility to allow non-parametric fits with relaxed assumptions on the actual relationship between response and predictor, provides the potential for better fits to data than purely parametric models, but arguably with some loss of interpretablity.".
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- Generalized_additive_model wikiPageExternalLink index.html.
- Generalized_additive_model wikiPageID "3608284".
- Generalized_additive_model wikiPageRevisionID "597676316".
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- Generalized_additive_model subject Category:Generalized_linear_models.
- Generalized_additive_model subject Category:Nonparametric_regression.
- Generalized_additive_model comment "In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.GAMs were originally developed by Trevor Hastie and Robert Tibshirani to blend properties of generalized linear models with additive models.The model relates a univariate response variable, Y, to some predictor variables, xi.".
- Generalized_additive_model label "Generalized additive model".
- Generalized_additive_model label "Modèle additif généralisé".
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- Generalized_additive_model sameAs m.09pn4y.
- Generalized_additive_model sameAs Q3318054.
- Generalized_additive_model sameAs Q3318054.
- Generalized_additive_model wasDerivedFrom Generalized_additive_model?oldid=597676316.
- Generalized_additive_model isPrimaryTopicOf Generalized_additive_model.