Vinai George Biju, Prashant CM,



Generalized Additive Model,Newton Approximation, Laplace,Diabetic Retinopathy,


The Generalized Additive Model is found to be a convenient framework due of its flexibility in non-linear predictor specification.  It is possible to combine several forms of smooth plus Gaussian random effects and use numerically accurate and wide-ranging fitting smoothness estimates. The Newton interpretation of smoothing provides standardized interval approximations.  The Model assortment through additional selection penalties and p-value estimates is proposed along with bivariate combination of input variables capturing different non-linear relationship. The proposed extension includes, using non-exponential family distribution, orderly categorical models, negative binomial distributions, and multivariate additive models, log-likelihood based on Laplace and Newton models. The general problem is that there is not one particular architecture do everything with an exponential GAM family.


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