Background With progress on both the theoretical and the computational fronts the use of
spline modelling has become an established tool in statistical regression analysis. An …

Load forecasting has been a fundamental business problem since the inception of the
electric power industry. Over the past 100 plus years, both research efforts and industry …

The integrated nested Laplace approximation (INLA) is a recent computational method that
can fit Bayesian models in a fraction of the time required by typical Markov chain Monte …

This article discusses a general framework for smoothing parameter estimation for models
with regular likelihoods constructed in terms of unknown smooth functions of covariates …

For spline regressions, it is well known that the choice of knots is crucial for the performance
of the estimator. As a general learning framework covering the smoothing splines, learning …

In the absence of annual laminations, time series generated from lake sediments or other
similar stratigraphic sequences are irregularly spaced in time, which complicates formal …

In this paper, we discuss an extension to two popular approaches to modeling complex
structures in ecological data: the generalized additive model (GAM) and the hierarchical …

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 …

This book is about learning from data using the Generalized Additive Models for Location,
Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and …

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …