Non-linear relationships are accommodated in a regression model using smoothing functions. Interaction may occurs between continuous variable, in this case interaction between nonlinear and linear covariate leads to varying coefficent model (VCM), a subclass of generalized additive model. Additive models can be estimated as generalized linear mixed models, after being reparametrized. In this article we show three different type of matrix design for mixed model for VCM, by applying b-spline smoothing functions. An application on real data is provided and model estimates re computed with a Bayesian approach
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
Summary. Generalized additive mixed models are proposed for overdispersed and correlated data, which...
We present a novel method for the estimation of variance parameters in generalised linear mixed mode...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
Summary. Generalized additive mixed models are proposed for overdispersed and correlated data, which...
We present a novel method for the estimation of variance parameters in generalised linear mixed mode...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
Varying-coefficient models provide a flexible framework for semi- and nonparametric generalized regr...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been...
Summary. Generalized additive mixed models are proposed for overdispersed and correlated data, which...
We present a novel method for the estimation of variance parameters in generalised linear mixed mode...