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A review of optimal designs in survey sampling
Author(s) -
Bellhouse David R.
Publication year - 1984
Publication title -
canadian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.804
H-Index - 51
eISSN - 1708-945X
pISSN - 0319-5724
DOI - 10.2307/3314724
Subject(s) - nonprobability sampling , statistics , sampling (signal processing) , section (typography) , sampling design , selection (genetic algorithm) , systematic sampling , mathematics , survey sampling , econometrics , computer science , medicine , machine learning , population , filter (signal processing) , computer vision , operating system , environmental health
Results in five areas of survey sampling dealing with the choice of the sampling design are reviewed. In Section 2, the results and discussions surrounding the purposive selection methods suggested by linear regression superpopulation models are reviewed. In Section 3, similar models to those in the previous section are considered; however, random sampling designs are considered and attention is focused on the optimal choice of π j . Then in Section 4, systematic sampling methods obtained under autocorrelated superpopulation models are reviewed. The next section examines minimax sampling designs. The work in the final section is based solely on the randomization. In Section 6 methods of sample selection which yield inclusion probabilities π j = n/N and π ij = n ( n ‐ 1)/ N ( N ‐ 1), but for which there are fewer than N C n possible samples, are mentioned briefly.