Multivariate spatial smoothing using additive regression splines

Multivariate Adaptive Regression 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...

Discussion: Multivariate Adaptive Regression Splines

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Bayesian Analysis of Multivariate Smoothing Splines

A general version of multivariate smoothing splines with correlated errors and correlated curves is proposed. A suitable symmetric smoothing parameter matrix is introduced, and practical priors are developed for the unknown covariance matrix of the errors and the smoothing parameter matrix. An efficient algorithm for computing the multivariate smoothing spline is derived, which leads to an effi...

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...