MODEL ADITIF TERGENERALISASI - , NURLIA HIKMANANDA ([Yogyakarta] : Universitas Gadjah Mada, 2013)
Modeling relationship between respond variable and predictor is not always following linearity and normality assumption. Hastie and Tibshirani (1986) adapted additive models to generalized linear models that is called by generalized additive models. Generalized additive models is modeling technique that appropriates for overcomes nonlinearity in the relationship between respond variable and predictor, and does not limited respond variable to normal distribution but other distributions in the exponential family allowing to use in this model. Generalized additive models replace linear component on the generalized linear models with sum of functions which is estimated using local scoring algorithm. Additive component in generalized additive models is sum of univariat function of every predictors, so we can see contribution of each predictor to respond. Illustration is given in case study using R software.