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 …

In the past two decades a parametric multivariate regression modelling approach for
analyzing growth curve data has achieved prominence. The approach, which has several …

ASReml-R is a statistical package that fits linear mixed models using Residual Maximum
Likelihood (REML) in the R environment. This package uses the same computational kernel …

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 …

Multiple Imputation and its Application The most up-to-date edition of a bestselling guide to
analyzing partially observed data In this comprehensively revised Second Edition of Multiple …

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 …

The majority of data sets collected by researchers in all disciplines are multivariate, meaning
that several measurements, observations, or recordings are taken on each of the units in the …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

A general class of statistical models for a univariate response variable is presented which
we call the generalized additive model for location, scale and shape (GAMLSS). The model …

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