The Annals of Statistics · 1977 · 27 citations · 5 references
Natural SciencesMathematicsCombinatoricsDecision TheoryProbability TheoryConvex AnalysisInferential StatisticsProbability InequalitiesSampling DesignSampling TheoryStatistical InferenceConvex SetsFinite Population SamplingProbability Inclusion-exclusionRandom SamplingSampling-based Decision MakingSimple Random SamplingUpper Bound Computation
Let $P_1$ be a finite population sampling plan and $V$ a collection of subsets of units. The inclusion probabilities for members of $V$ may be calculated. For example, if $V$ comprises all single units and pairs of units we obtain all first and second order inclusion probabilities $\pi_i, \pi_{ij}$. Another plan $P_2$ is called equivalent to $P_1$ with respect to $V$ if the corresponding inclusion probabilities for $P_1$ are equal to those for $P_2$. However, $P_2$ may have fewer samples with positive probability of selection, that is to say smaller "support." An upper bound is put on the minimum support size of all such $P_2$. For $P_1$ simple random sampling, some examples are given for $P_2$ with small support.
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J. Kiefer, J. Wolfowitz · The Annals of Mathematical Statistics · 1959 · 799 citations · Full text
Randomization and Experimentation
W. J. Youden · Technometrics · 1972 · 54 citations