List Sequential Sampling with Equal or Unequal Probabilities without Replacement

Sunter, A. B.

doi:10.2307/2346966

Cited by 46 publications

(20 citation statements)

References 2 publications

(4 reference statements)

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“…An extensive review and bibliography by Hanif & Brewer (1980) catalogues and classifies 50 published methods none of which gives a completely satisfactory solution and none of which emerges, at least for a sample size greater than two, as a practical alternative to the usual method of systematic sampling with probability proportional to size. The present paper is a further development of the author's earlier paper (Sunter, 1977a) on list-sequential sampling without replacement.…”

Section: Introductionmentioning

confidence: 90%

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Solutions to the Problem of Unequal Probability Sampling without Replacement

Sunter¹

1986

International Statistical Review / Revue Internationale de Stat

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JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. International Statistical Institute (ISI) is collaborating with JSTOR to digitize, preserve and extend access to International Statistical Review / Revue Internationale de Statistique. SummaryThe problem of sampling with probability proportional to a measure of size and with certain requirements for the probabilities of joint selection of pairs of units has a history that goes back to about 1943. However, although more than 50 articles on the subject have been published none of them has given a completely satisfactory solution and none of them has emerged, at least for sample sizes greater than two, as a practical alternative to the usual method of systematic sampling with probability proportional to size. The solution given in this paper is exact, simple and intuitively appealing.

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Section: Introductionmentioning

confidence: 90%

“…Proofs of (7) and (8) are given by Sunter (1977a). (There are some misprints in that paper, which the reader will have no difficulty in correcting.)…”

Section: Selection When Sample Size Is Not Fixedmentioning

confidence: 99%

Solutions to the Problem of Unequal Probability Sampling without Replacement

Sunter¹

1986

International Statistical Review / Revue Internationale de Stat

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“…The inclusion probabilities have been computed using the auxiliary variable z and the other auxiliary variable x has been used at the estimation stage. From each of the large population, a sample of size n (depending on the population size) was drawn whereas a sample of size n = 6 was drawn from small populations using Sunter's sampling scheme [26,27]. The large populations and small populations are listed in Table A1 of Appendix 1 and Table A2 of Appendix 2, respectively .…”

Section: Simulation Studymentioning

confidence: 99%

A Monte Carlo comparison of some variance estimators of the Horvitz–Thompson estimator

Patel

2010

Journal of Statistical Computation and Simulation

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At the design and estimation stage of a survey, large survey organization often uses auxiliary information. This article discusses various procedures for improving variance estimation of the Horvitz-Thompson estimator of a finite population total with the aid of auxiliary information. To study the design-based properties of the proposed variance estimators relative to the standard one, a small scale Monte Carlo study is performed.

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“…VAROPT [6,1,30] is a generic weight summarization scheme for aggregated data that has optimality properties that date back to [28]. We leverage VAROPT in our modular summarization as the internal sampler at all nodes of an IFT.…”

Section: Variance Optimal Summaries: Varoptmentioning

confidence: 99%

Composable, scalable, and accurate weight summarization of unaggregated data sets

et al. 2009

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Many data sets occur as unaggregated data sets, where multiple data points are associated with each key. In the aggregate view of the data, the weight of a key is the sum of the weights of data points associated with the key. Examples are measurements of IP packet header streams, distributed data streams produced by events registered by sensor networks, and Web page or multimedia requests to context distribution servers. We aim to combine sampling and aggregation to provide accurate and efficient summaries of the aggregate view. However, data points are scattered in time or across multiple servers and hence aggregation is subject to resource constraints on the size of summaries that can be stored or transmitted.We develop a summarization framework for unaggregated data where summarization is a scalable and composable operator, and as such, can be tailored to meet resource constraints. Our summaries support unbiased estimates of the weight of subpopulations of keys specified using arbitrary selection predicates. While we prove that under such scenarios there is no variance optimal scheme, our estimators have the desirable properties that the variance is progressively closer to the minimum possible when applied to a "more" aggregated data set. An extensive evaluation using synthetic and real data sets shows that our summarization framework outperforms all existing schemes for this fundamental problem, even for the special and well-studied case of data streams.

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List Sequential Sampling with Equal or Unequal Probabilities without Replacement

Cited by 46 publications

References 2 publications

Solutions to the Problem of Unequal Probability Sampling without Replacement

Solutions to the Problem of Unequal Probability Sampling without Replacement

A Monte Carlo comparison of some variance estimators of the Horvitz–Thompson estimator

Composable, scalable, and accurate weight summarization of unaggregated data sets

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