Recent work by Reiss and Ogden provides a theoretical basis for sometimes preferring
restricted maximum likelihood (REML) to generalized cross-validation (GCV) for smoothing …

The first edition of this book has established itself as one of the leading references on
generalized additive models (GAMs), and the only book on the topic to be introductory in …

As competition in the biopharmaceutical market gets keener due to the market entry of
biosimilars, process analytical technologies (PATs) play an important role for process …

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

Structured additive regression models are perhaps the most commonly used class of models
in statistical applications. It includes, among others,(generalized) linear …

The idea for this book came from the time the authors spent at the Statistics and Applied
Mathematical Sciences Institute (SAMSI) in Research Triangle Park in North Carolina …

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 …

With contributions from some of the most prominent researchers in the field, this carefully
edited collection provides a clear, comprehensive, and unified overview of recent …

The biopharmaceutical industry is still running in batch mode, mostly because it is highly
regulated. In the past, sensors were not readily available and in‐process control was mainly …

Existing computationally efficient methods for penalized likelihood generalized additive
model fitting employ iterative smoothness selection on working linear models (or working …