SpringerBriefs in Statistics Mixed-Effects Models and Small Area Estimation - Shonosuke Sugasawa Tatsuya Kubokawa Kartoniert (TB)

This book provides a self-contained introduction of mixed-effects models and small area estimation techniques. In particular it focuses on both introducing classical theory and reviewing the latest methods. First basic issues of mixed-effects models such as parameter estimation random effects prediction variable selection and asymptotic theory are introduced. Standard mixed-effects models used in small area estimation known as the Fay-Herriot model and the nested error regression model are then introduced. Both frequentist and Bayesian approaches are given to compute predictors of sma ll area parameters of interest. For measuring uncertainty of the predictors several methods to calculate mean squared errors and confidence intervals are discussed. Various advanced approaches using mixed-effects models are introduced from frequentist to Bayesian approaches. This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.