Linear mixed‐effects models are powerful tools for analysing complex datasets with
repeated or clustered observations, a common data structure in ecology and evolution …

Context. Aims. Active galactic nuclei are known to have complex X-ray spectra that depend
on both the properties of the accreting super-massive black hole (eg mass, accretion rate) …

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive
manual for the applied researcher who wants to perform data analysis using linear and …

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical
analyses from a Bayesian perspective. Using examples largely from the authors' own …

An accessible introduction to indirect estimation methods, both traditional and model-based.
Readers will also find the latest methods for measuring the variability of the estimates as …

The growth of biostatistics has been phenomenal in recent years and has been marked by
considerable technical innovation in both methodology and computational practicality. One …

This article presents a simulation-based method designed to establish the computational
correctness of software developed to fit a specific Bayesian model, capitalizing on properties …

The use of Markov chain Monte Carlo (MCMC) methods for estimating hierarchical models
involves complex data structures and is often described as a revolutionary development. An …

Model checking in item response theory (IRT) is an underdeveloped area. There is no
universally accepted tool for checking IRT models. The posterior predictive model-checking …

An intermediate-level treatment of Bayesian hierarchical models and their applications, this
book demonstrates the advantages of a Bayesian approach to data sets involving inferences …