Graph sampling

Zhang, Li‐Chun; Patone, Martina

doi:10.1007/s40300-017-0126-y

Cited by 11 publications

(25 citation statements)

References 18 publications

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“…sample graphs, which can be taken from the given population graph, according to a specified method of sampling. Zhang and Patone (2017) synthesise the existing graph sampling theory, extending the previous works on this topic by Frank (1971Frank ( , 1980aFrank ( ,b, 2011. A general definition is given for probability sample graphs, in a manner that is similar to general probability samples from a finite population (Neyman, 1934); and the unbiased Horvitz-Thompson (HT) estimator is developed for arbitrary T -stage snowball sampling from finite graphs, as in finite population sampling (Horvitz and Thompson, 1952).…”

Section: Introductionmentioning

confidence: 76%

“…A general definition is given for probability sample graphs, in a manner that is similar to general probability samples from a finite population (Neyman, 1934); and the unbiased Horvitz-Thompson (HT) estimator is developed for arbitrary T -stage snowball sampling from finite graphs, as in finite population sampling (Horvitz and Thompson, 1952). To this end the observation procedure of graph sampling must be ancestral (Zhang and Patone, 2017), in that one needs to know which other out-of-sample nodes could have led to the observed motifs in the sample graph, had they been selected in the initial sample of nodes. Under T -stage snowball sampling, additional stages of sampling are generally needed in order to identify the ancestors of all the motifs observed by the T -th stage.…”

Section: Introductionmentioning

confidence: 99%

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Bipartite incidence graph sampling and weighting

Zhang¹

2021

Graph Sampling

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Graph sampling is a statistical approach to study real graphs, which represent the structure of many technological, social or biological phenomena of interest. We develop bipartite incident graph sampling (BIGS) as a feasible representation of graph sampling from arbitrary finite graphs. It provides also a unified treatment of the existing unconventional sampling methods which were studied separately in the past, including indirect, network and adaptive cluster sampling. The sufficient and necessary conditions of feasible BIGS representation are established, given which one can apply a family of Hansen-Hurwitz type design-unbiased estimators in addition to the standard Horvitz-Thompson estimator. The approach increases therefore the potentials of efficiency gains in graph sampling. A general result regarding the relative efficiency of the two types of estimators is obtained. Numerical examples are given to illustrate the versatility of the proposed approach.

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Section: Introductionmentioning

confidence: 76%

Section: Introductionmentioning

confidence: 99%

Bipartite incidence graph sampling and weighting

Zhang¹

2021

Graph Sampling

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“…that target informativeness ( [2,6,7]) and nonsampling errors such as nonresponse ( [4,6]). All contributions account for the complexity of the sampling design and make use of models.…”

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confidence: 99%

“…Zhang and Patone [7] look at sampling when units exhibit a network structure. In particular, they provide a systematic review of the theory for graph sampling and propose a definition of sample graph together with the relevant observation procedures that enable sampling in a graph.…”

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confidence: 99%

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Foreword to the special issue on “Advances in Survey Statistics”

Opsomer

Ranalli

Dickson

2017

METRON

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This Special Issue was inspired by the desire to bring together valuable and modern contributions in the field of Survey Statistics, which shed light on some of the challenging tasks of designing and analyzing surveys. Traditionally, survey statistics has been seen as a separate branch to the rest of statistics, because of its reliance on the design-based approach to inference, as opposed to the model-based approach used in other applications of statistics. The former approach is certainly sensible for survey data, because it is common for design features such as strata, clusters and unequal probabilities to induce strong biasing effects in the data. While these effects can be incorporated in models, an often simpler and more robust approach is to rely on randomization theory and inverse-probability weighting to produce estimates and perform inference.However, there is also a need to recognize the many interactions between randomizationbased inference and statistical modeling. The role of modeling in regression estimation is well understood and has been incorporated into the design-based framework for several decades. At the other extreme, small area estimation methods are fully model-based, with the design effects incorporated through the model or, when appropriate, ignored. But there are many statistical research topics that occur at the intersection of randomization-based and model-based inference, as illustrated by many of the contributions to this Special Issue. These include nonresponse and measurement errors in survey data, estimation under non-measurable designs, and fitting of statistical models under design informativeness. The contributions of this Special Issue deal with topics in either design ([1,7]) or estimation ([2-6]), also including approaches B M. Giovanna Ranalli

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Graph sampling by lagged random walk

Zhang

2022

Stat

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We propose a family of lagged random walk sampling methods in simple undirected graphs, where transition to the next state (i.e., node) depends on both the current and previous states—hence, lagged. The existing random walk sampling methods can be incorporated as special cases. We develop a novel approach to estimation based on lagged random walks at equilibrium, where the target parameter can be any function of values associated with finite‐order subgraphs, such as edge, triangle, 4‐cycle and others.

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Graph sampling

Cited by 11 publications

References 18 publications

Bipartite incidence graph sampling and weighting

Bipartite incidence graph sampling and weighting

Foreword to the special issue on “Advances in Survey Statistics”

Graph sampling by lagged random walk

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