Introduction

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.

Basic Issues of Mixed-Effects Models

First, basic issues of mixed-effects models, such as parameter estimation, random effects prediction, variable selection, and asymptotic theory, are introduced.

Standard Models in Small Area Estimation

Standard mixed-effects models used in small area estimation, known as the Fay-Herriot model and the nested error regression model, are then introduced.

Approaches to Computing Predictors

Both frequentist and Bayesian approaches are given to compute predictors of small area parameters of interest.

Measuring Uncertainty

For measuring uncertainty of the predictors, several methods to calculate mean squared errors and confidence intervals are discussed.

Advanced Approaches

Various advanced approaches using mixed-effects models are introduced, from frequentist to Bayesian approaches.

Target Audience

This book is helpful for researchers and graduate students in fields requiring data analysis skills as well as in mathematical statistics.