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Prediction theory for finite populations. (English) Zbl 0751.62003
Springer Series in Statistics. New York etc.: Springer-Verlag. xi, 207 p. (1992).
This book is a systematic treatment of prediction theory for finite populations based on superpopulation models, and brings together material scattered in papers during the last two decades. After a brief outline of ideas and principles, the following topics are dealt with in separate chapters:
Optimal predictors of population quantities; Bayes and minimax predictors; maximum likelihood predictors; classical and Bayes prediction intervals; the effects of model misspecification; conditions for robustness and Bayesian modelling; models with measurement errors; asymptotic properties in finite populations, design characteristics of predictors.
The emphasis is on the analysis of the data rather than on the design.

MSC:
62D05 Sampling theory, sample surveys
62F15 Bayesian inference
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