Ismo Teikari · 2008
A response burden arises from the need for
statistical information about finite populations. In
business surveys the response burden is an important
aspect since one business may fall in surveys many
times in a given time interval. This raises a
question. How to even out this burden as fairly as
possible? Poisson sampling, with Bernoulli sampling
and Bernoullis sampling and strict Poisson sampling
as special cases, has been found in the early
seventies to have good properties as regards sample
co-ordination.
A new approach for sample co-ordination, the Poisson
Mixture (PoMix) sampling is introduced in this
study. This is a sampling scheme which partly uses
Bernoulli sampling scheme and partly Strict Poisson
sampling scheme. This study also proves this
sampling scheme to be more efficient than the
traditional Poisson Sampling.
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2009
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Martin, Steve · Zenodo (CERN European Organization for Nuclear Research) · 2025
Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, ), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, ).
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Gergely Flamich · 2023
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Martin, Steve · Zenodo (CERN European Organization for Nuclear Research) · 2025
Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, ), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, ).
Cited by 0Open access
Steve Martin · 2021
Sequential Poisson sampling is a variation of Poisson sampling for drawing probability-proportional-to-size samples with a given number of units, and is commonly used for price-index surveys. This package gives functions to draw stratified sequential Poisson samples according to the method by Ohlsson (1998, ISSN:0282-423X), as well as other order sample designs by Rosén (1997, <<a href="https://doi.org/10.1016%2FS0378-3758%2896%2900186-3" target="_top">doi:10.1016/S0378-3758(96)00186-3</a>>), and generate approximate bootstrap replicate weights according to the generalized bootstrap method by Beaumont and Patak (2012, <<a href="https://doi.org/10.1111%2Fj.1751-5823.2011.00166.x" target="_top">doi:10.1111/j.1751-5823.2011.00166.x</a>>).
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Wilmer Prentius · Environmetrics · 2023
Abstract Designs that produces spatially balanced, or well‐spread, samples are desirable as they increase the probability of obtaining a sample highly representative of the population. Spatially correlated Poisson sampling (SCPS) is a method for selecting well‐spread samples. In the SCPS method, the sampling outcomes (inclusion or exclusion of units) are decided sequentially. After each decision, the inclusion probabilities of surrounding units are updated. A specific order for deciding the sampling outcomes is not enforced for SCPS, that is, the order can be chosen randomly or be fixed. A new modified method called locally correlated Poisson sampling (LCPS) is suggested. In this new method, the order of the decisions makes sure the inclusion probabilities are updated (more) locally. As a result, a stronger negative correlation between inclusion indicators of nearby units is achieved. Simulations on various data sets show that the resulting samples from LCPS, in general, are more spatially balanced and produce lower variance than samples from SCPS and the local pivotal method.
Cited by 1Open access
Scott A. Mitchell, Patrick Knupp, Mohamed S. Ebeida et al. · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2016
Abstract not provided.
Cited by 2Open access
Hongwei Li · 2010
Poisson disk sampling : modern techniques by Hongwei Li thesis 2010 x, 98 p. : ill. ; 30 cm Poisson disk sampling has proven to be very useful and versatile in a variety of computer graphics applications in the past…Read more ›
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Liese Bekkers, Frank Neven, Lorrens Pantelis et al. · arXiv (Cornell University) · 2026
We introduce the problem of Poisson sampling over joins: compute a sample of the result of a join query by conceptually performing a Bernoulli trial for each join tuple, using a non-uniform and tuple-specific probability. We propose an algorithm for Poisson sampling over acyclic joins that is nearly instance-optimal, running in time O(N + k \log N) where N is the size of the input database, and k is the size of the resulting sample. Our algorithm hinges on two building blocks: (1) The construction of a random-access index that allows, given a number i, to randomly access the i-th join tuple without fully materializing the (possibly large) join result; (2) The probing of this index to construct the result sample. We study the engineering trade-offs required to make both components practical, focusing on their implementation in column stores, and identify best-performing alternatives for both. Our experiments on real-world data demonstrate that this pair of alternatives significantly outperforms the repeated-Bernoulli-trial algorithm for Poisson sampling while also demonstrating that the random-access index by itself can be used to competively implement Yannakakis' acyclic join processing algorithm when no sampling is required. This shows that, as far a query engine design is concerned, it is possible to adopt a uniform basis for both classical acyclic join processing and Poisson sampling, both without regret compared to classical join and sampling algorithms.
Cited by 0Open access
Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2010
Poisson disk sampling : modern techniques by Hongwei Li thesis 2010 x, 98 p. : ill. ; 30 cm Poisson disk sampling has proven to be very useful and versatile in a variety of computer graphics applications in the past…Read more ›
Cited by 0
Liese Bekkers, Frank Neven, Lorrens Pantelis et al. · ArXiv.org · 2026
We introduce the problem of Poisson sampling over joins: compute a sample of the result of a join query by conceptually performing a Bernoulli trial for each join tuple, using a non-uniform and tuple-specific probability. We propose an algorithm for Poisson sampling over acyclic joins that is nearly instance-optimal, running in time O(N + k \log N) where N is the size of the input database, and k is the size of the resulting sample. Our algorithm hinges on two building blocks: (1) The construction of a random-access index that allows, given a number i, to randomly access the i-th join tuple without fully materializing the (possibly large) join result; (2) The probing of this index to construct the result sample. We study the engineering trade-offs required to make both components practical, focusing on their implementation in column stores, and identify best-performing alternatives for both. Our experiments on real-world data demonstrate that this pair of alternatives significantly outperforms the repeated-Bernoulli-trial algorithm for Poisson sampling while also demonstrating that the random-access index by itself can be used to competively implement Yannakakis' acyclic join processing algorithm when no sampling is required. This shows that, as far a query engine design is concerned, it is possible to adopt a uniform basis for both classical acyclic join processing and Poisson sampling, both without regret compared to classical join and sampling algorithms.
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Michael McCool, Eugene Fiume · Canada Human-Computer Communications Society · 1992
Cited by 153Open access
Weize Quan, Dong‐Ming Yan, Jianwei Guo et al. · 2016
Maximal Poisson-disk Sampling (MPS) is a fundamental research topic in computer graphics. An ideal MPS pattern should satisfy three properties: bias-free, minimal distance, maximal coverage. The classic approach for generating MPS is dart throwing, but this method is unable to precisely control the number of samples when achieving maximality [Ebeida et al. 2011]. Sample elimination [Yuksel 2015] is an recently proposed algorithm that could generate Poisson-disk sets with an exactly desired size, but it cannot guarantee the maximal coverage. In this work, we propose a simple 2D MPS algorithm that can precisely control the number of samples, while meeting all three criteria simultaneously. Unlike previous conflict-based methods, our algorithm controls the number of samples by dynamically adjusting sampling radius.
Cited by 5
Dawit Solomon, Roger R. Davidson · eCommons (Cornell University) · 1972
12 pages, 1 article
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Zekai Xu · Figshare · 2015
Supporting Information files of the paper
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Xu, Zekai · Figshare · 2015
Supporting Information files of the paper
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SIAM eBooks · 2011
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Paweł Kasprzak, Mateusz Urbańczyk, Krzysztof Kazimierczuk · Journal of Biomolecular NMR · 2021
Non-uniform sampling (NUS) is a popular way of reducing the amount of time taken by multidimensional NMR experiments. Among the various non-uniform sampling schemes that exist, the Poisson-gap (PG) schedules are particularly popular, especially when combined with compressed-sensing (CS) reconstruction of missing data points. However, the use of PG is based mainly on practical experience and has not, as yet, been explained in terms of CS theory. Moreover, an apparent contradiction exists between the reported effectiveness of PG and CS theory, which states that a "flat" pseudo-random generator is the best way to generate sampling schedules in order to reconstruct sparse spectra. In this paper we explain how, and in what situations, PG reveals its superior features in NMR spectroscopy. We support our theoretical considerations with simulations and analyses of experimental data from the Biological Magnetic Resonance Bank (BMRB). Our analyses reveal a previously unnoticed feature of many NMR spectra that explains the success of "blue-noise" schedules, such as PG. We call this feature "clustered sparsity". This refers to the fact that the peaks in NMR spectra are not just sparse but often form clusters in the indirect dimension, and PG is particularly suited to deal with such situations. Additionally, we discuss why denser sampling in the initial and final parts of the clustered signal may be useful.
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Wenguang Hou (730572), Zekai Xu (5663086), Nannan Qin (5663089) et al. · Figshare · 2015
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