We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as being either zero, ...
These results are not used in the model selection process, which is based on AIC values, but are helpful for understanding the effects and interpreting the selected models. For GAM's, we can consider frequentist or Bayesian inference for the effects. The typical frequentist variancecovariance for...
2. Nested iteration, defines a λ selection criterion in terms of the model deviance and optimizes it directly. Each evaluation of the criterion requires an ‘inner’ PIRLS to obtain ˆ β λ . This converges, since a properly defined function of λ is optimized. The second option ...
Span size selectionGeneralized additive models (GAMs) have distinct advantages over generalized linear models as they allow investigators to make inferences about associations between outcomes and predictors without placing parametric restrictions on the associations. The variable of interest is often smoothed...
Variable selectiongeneralized additive modelssingle index modelslink function estimationThe generalized additive model is a well established and strong tool that allows modelling smooth effects of predictors on the response. However, if the link function, which is typically chosen as the canonical link, ...
1.5.3Modelselection30 1.5.4Anothermodelselectionexample31 Afollowup34 1.5.5Con,denceintervals35 1.5.6Prediction36 1.6Practicalmodellingwithfactors36 1.6.1Identi,ability37 1.6.2Multiplefactors39 1.6.3‘Interactions’offactors40 1.6.4UsingfactorvariablesinR41 ...
We introduce an extension of the generalized additive model which accounts for non-random sample selection by using a selection equation. The proposed approach allows for different distributions of the outcome variable, various dependence structures between the (outcome and selection) equations through ...
importosimportnumpyasnpimporttensorflowastffromsklearn.preprocessingimportMinMaxScalerfromsklearn.model_selectionimporttrain_test_splitfromgaminetimportGAMINetfromgaminet.utilsimportlocal_visualizefromgaminet.utilsimportglobal_visualize_densityfromgaminet.utilsimportfeature_importance_visualizefromgaminet.utilsimportplot...
A rule for the automatic selection of the smoothing parameters, suitable for data mining of large datasets, is derived. The wavelet-based method is then extended to estimate generalized additive models. A primal-dual log-barrier interior point algorithm is proposed to solve the corresponding convex...
We introduce GAMSEL (Generalized Additive Model Selection), a penalized likelihood approach for fitting sparse generalized additive models in high dimension. Our method interpolates between null, linear and additive models by allowing the effect of each variable to be estimated as being either zero, ...