<![CDATA[The additive model is the generalized of multiple linear regression that expresses the mean of areponse variable as a sum of functional form of predictors. The widely used estimation of additivemodels described in Hastie and Tibshirani (1990) is backfitting algorithm. However, the algorithmwith linear smoothers gave some difficulties when it comes to model selection and its inference. Theadditive model with P-spline as smooth function admits a mixed model formulation, in whichvariance components control the non-linearity degree in the smooth function. This research isfocused in comparing of estimation additive models using backfitting algorithm and linear mixedmodel approach. The research results show that estimation of additive models using linear mixedmodels offer simplicity in the computation, since use low-rank dimension of P-spline, and in themodel inference, since based on maximum likelihood method. Estimation additive model using linearmixed model approach can be suggested as an alternative method beside backfitting algorithm]]>
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