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NON LINEAR GENERALIZED ADDITIVE MODELS USING LIKELIHOOD ESTIMATIONS WITH LAPLACE AND NEWTON APPROXIMATIONS
2020
JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES
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
doi:10.26782/jmcms.2020.07.00021
fatcat:oxcvixj3urdzxoag2dbmrsyzly