Presents the theory of linear statistical models at a level appropriate for senior undergraduate or first-year graduate students. This book also presents the basic theory behind linear statistical models with motivation from an algebraic as well as a geometric perspective.[...]
This accessible reference includes selected contributions from Bayesian Thinking - Modeling and Computation, Volume 25 in the Handbook of Statistics Series, with a focus on key methodologies and applications for Bayesian models and computation. It describes parametric and nonparametric Bayesian meth[...]