Penalized splines have gained much popularity as a flexible tool for smoothing and semi‐
parametric models. Two approaches have been advocated:(1) use a B‐spline basis, equally …

We provide a sensitivity analysis toolbox consisting of a set of Matlab functions that offer
utilities for quantifying the influence of uncertain input parameters on uncertain model …

Machine learning is an important task for learning artificial neural networks, and we find in
the learning one of the common problems of learning the Artificial Neural Network (ANN) is …

Semiparametric regression is concerned with the flexible incorporation of non-linear
functional relationships in regression analyses. Any application area that benefits from …

It's been over a decade since the first edition of Measurement Error in Nonlinear Models
splashed onto the scene, and research in the field has certainly not cooled in the interim. In …

INLA stands for Integrated Nested Laplace Approximations, which is a new method for fitting
a broad class of Bayesian regression models. No samples of the posterior marginal …

Computer simulations based on mathematical models have become ubiquitous across the
engineering disciplines and throughout the physical sciences. Successful use of a …

P-splines are an attractive approach for modeling nonlinear smooth effects of covariates
within the additive and varying coefficient models framework. In this article, we first develop …

Penalized splines, or P-splines, are regression splines fit by least-squares with a roughness
penalty. P-splines have much in common with smoothing splines, but the type of penalty …

Drawing from the authors' own work and from the most recent developments in the field,
Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity …