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Two-Stage Importance Sampling With Mixture Proposals
For importance sampling (IS), multiple proposals can be combined to address different aspects of a target distribution. There are various methods for IS with multiple proposals, including Hesterberg's stratified IS estimator, Owen and Zhou's regression estimator, and Tan's maximum likelihood estimator.
Wentao Li, Zhiqiang Tan, Rong Chen
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Adjusted two-stage adaptive cluster sampling
Applied Mathematics and Computation, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
A. Khan, Hassen A. Muttlak
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ON TWO‐STAGE SUCCESSIVE SAMPLING
Australian Journal of Statistics, 1969SummaryIn repeated sampling enquiries application of method of successive sampling with partial replacement of units is advantageous. In this paper use of successive sampling technique when the sampling design is multi‐stage has been studied. Estimates of population mean and its variance have been obtained (a) on the second occasion, and (b) on the h ...
Singh, D., Kathuria, O. P.
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Adaptive Cluster Sampling in Two-stage Sampling
Australian & New Zealand Journal of Statistics, 2014Summary: Adaptive cluster sampling can be a useful design for surveying rare and clustered populations. Here we present a new development in adaptive cluster sampling where we use a two-stage design and extend the complete allocation sampling method.
Moradi, Mohammad +2 more
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TWO‐stage sampling in pharmaceutical applications
Statistics in Medicine, 1993AbstractIn the pharmaceutical industry, some tests for quality assurance before, during and after the manufacture of a drug product involve a two‐stage sampling technique. We propose statistical testing procedures in a two‐stage sampling problem with large within‐class sample sizes.
J, Shao, S C, Chow
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Two-stage designs with small sample sizes
Journal of Biopharmaceutical Statistics, 2022When applying group-sequential designs in clinical trials with normally distributed outcomes, approximate critical values are often applied. Here, normally distributed test statistics are assumed which, however, are in fact t-distributed. For small sample sizes, the approximation may lead to a serious inflation of the type I error rate.
Meinhard Kieser +2 more
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