The Linear Least-Squares Prediction Approach to Two-Stage Sampling
作者:
RichardM. Royall,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1976)
卷期:
Volume 71,
issue 355
页码: 657-664
ISSN:0162-1459
年代: 1976
DOI:10.1080/01621459.1976.10481542
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
The linear least-squares prediction approach is applied to some problems in two-stage sampling from finite populations. A theorem giving the optimal (BLU) estimator and its error-variance under a general linear “superpopulation” model for a finite population is stated. This theorem is then applied to a model describing many populations whose elements are grouped naturally in clusters. Next, the probability model is used to analyze various conventional estimators and certain estimators suggested by the theory as alternatives to the conventional ones. Problems of design are considered, as are some consequences of regression-model failure.
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