In basic neuroscience research, data are often clustered or collected with repeated
measures, hence correlated. The most widely used methods such as t test and ANOVA do …

Statistical signal processing applications usually require the estimation of some parameters
of interest given a set of observed data. These estimates are typically obtained either by …

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 …

Generalized linear mixed models provide a flexible framework for modeling a range of data,
although with non-Gaussian response variables the likelihood cannot be obtained in closed …

Multilevel analysis is a methodology for the analysis of data with complex patterns of
variability, with a focus on nested sources of such variability–pupils in classes, employees in …

The only single-source——now completely updated and revised——to offer a unified
treatment of the theory, methodology, and applications of the EM algorithm Complete with …

Throughout the social, medical and other sciences the importance of understanding
complex hierarchical data structures is well understood. Multilevel modelling is now the …

Applauded for its clarity, this accessible introduction helps readers apply multilevel
techniques to their research. The book also includes advanced extensions, making it useful …

We consider the problem of comparing complex hierarchical models in which the number of
parameters is not clearly defined. Using an information theoretic argument we derive a …

The first edition of Analysis for Longitudinal Data has become a classic. Describing the
statistical models and methods for the analysis of longitudinal data, it covers both the …