We propose generalized additive partial linear models for complex data
which allow one to capture nonlinear patterns of some covariates, in the presence
of linear components. The proposed method improves estimation efficiency
and increases statistical power for correlated data through incorporating
the correlation information. A unique feature of the proposed...
We propose generalized additive partial linear models for complex data
which allow one to capture nonlinear patterns of some covariates, in the presence
of linear components. The proposed method improves estimation efficiency
and increases statistical power for correlated data through incorporating
the correlation information. A unique feature of the proposed...
We propose generalized additive partial linear models for complex data
which allow one to capture nonlinear patterns of some covariates, in the presence
of linear components. The proposed method improves estimation efficiency
and increases statistical power for correlated data through incorporating
the correlation information. A unique feature of the proposed...
The varying-coefficient model is flexible and powerful for modeling the dynamic changes of regression coefficients. It is important to identify significant covariates associated with response variables, especially for high-dimensional settings where the number of covariates can be larger than the sample size. We consider model selection in the high-dimensional setting...