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.
General
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