A large number of papers have appeared in the last twenty years on
estimating and predicting characteristics of finite populations. This
monograph is designed to present this modern theory in a systematic and
consistent manner. The authors' approach is that of superpopulation
models in which values of the population elements are considered as
random variables having joint distributions. Throughout, the emphasis is
on the analysis of data rather than on the design of samples. Topics
covered include: optimal predictors for various superpopulation models,
Bayes, minimax, and maximum likelihood predictors, classical and
Bayesian prediction intervals, model robustness, and models with
measurement errors. Each chapter contains numerous examples, and
exercises which extend and illustrate the themes in the text. As a
result, this book will be ideal for all those research workers seeking
an up-to-date and well-referenced introduction to the subject.