Inference in generalized additive mixed modelsby using smoothing splines

Inference in generalized additive mixed models by using smoothing splines

Generalized additive mixed models are proposed for overdispersed and correlated data, which arise frequently in studies involving clustered, hierarchical and spatial designs. This class of models allows ̄exible functional dependence of an outcome variable on covariates by using nonparametric regression, while accounting for correlation between observations by using random effects. We estimate no...

Bayesian hierarchical linear mixed models for additive smoothing splines

Bayesian hierarchical models have been used for smoothing splines, thin-plate splines, and L-splines. In analyzing high dimensional data sets, additive models and backfitting methods are often used. A full Bayesian analysis for such models may include a large number of random effects, many of which are not intuitive, so researchers typically use noninformative improper or nearly improper priors...

Improved inference in nonparametric regression using Lk-Smoothing splines

Smoothing splines are one of the most popular approaches to nonparametric regression. Wahba (J. Roy. Statist. Soc. Set. B 40 (1978) 364-372; 45 (1983) 133-150) showed that smoothing splines are also Bayes estimates and used the corresponding prior model to derive interval estimates for the regression function. Although the interval estimates work well on a global basis, they can have poor local...

Using Quantile Smoothing Splines to

The goal of this paper is to provide a statistically based deenition of employment subcenters for multicentric urban areas. In particular, we examine the shape of the employment density function using quantile smoothing splines as a nonparametric empirical speciication. This approach allows inspection of the employment gradient at the upper tail rather than the center of the employment density ...

On Mixed Interpolating-Smoothing Splines and the ν-Spline