Regression models have been important tools to study the association between outcome variables and their covariates. The traditional linear regression models usually specify such an association by the expectations of the outcome variables as function of the covariates and some parameters. In reality, however, interests often focus on their expectancies characterized by the conditional means. In this article, a new class of additive regression models is proposed to model the expectancies. The model parameters carry practical implication, which may allow the models to be useful in applications such as treatment assessment, resource planning or short-term forecasting. Moreover, the new model can be extended to include the outcome-dependent structure. Parametric and semiparametric methods are applied in the model estimation. Alternative models are discussed as well.


Biostatistics | Multivariate Analysis | Statistical Methodology | Statistical Theory