Sampling in Space and Time

Vos, J. W. E.

doi:10.2307/1401868

Cited by 8 publications

(5 citation statements)

References 1 publication

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“…By adding the time dimension to the deÿnition of the sampled population and to the sampling process, one accommodates the multiplicity issue by sampling in the dimension that created the issue in the ÿrst place, namely time. Although some basic theory for space and time sampling in road transportation studies has been developed [37], little has been done to compare the statistical e cacy of various space and time design adaptations to the problem of sampling mobile population groups.…”

Section: Strategies In Space and Time Samplingmentioning

confidence: 99%

Sampling minority groups in health surveys

Kalsbeek

2003

Statistics in Medicine

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Sampling racial and ethnic minority groups in population-based health studies presents the statistical designer with a variety of notable challenges, many of which can be traced to the manner in which the sampling frame maps into the set of group members. A brief review of sampling approaches aimed at race/ethnic groups is followed by findings on two particularly important statistical issues one may face when sampling minorities: the implications of the geographic diffusion of minorities within sampled area clusters, and the relative utility of different forms of space and time sampling to deal with sometimes dramatic change over time in the linkage between entries on a sampling frame and members of relatively mobile minority groups. Simulated findings from practical illustrations involving the sampling of Hispanics are presented to address each issue.

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Section: Strategies In Space and Time Samplingmentioning

confidence: 99%

Sampling minority groups in health surveys

Kalsbeek

2003

Statistics in Medicine

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“…Apart from the two key papers cited so far, the literature on the theory of cross-classified sampling is very limited. Vos (1964) provides some results for simple random sampling. There is a rather more extensive literature on the special case when the row and columns are ordered, typically in space, but possibly in time.…”

Section: Introductionmentioning

confidence: 99%

Cross-classified sampling: Some estimation theory

Skinner

2015

Statistics & Probability Letters

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“…Construction of the matrix P and evaluation of P 21 − 1/15: Table 2 gives the Monte Carlo estimates for the joint selection probabilities obtained with the three sampling algorithms in a design with M = 72, N = 9, n = 2 and an m vector given by (20,22,20,22,14,12,12,12,10). All the z-statistics comparing MCMC and hypergeometric joint selection probabilities are less than 2 in absolute value.…”

Section: Proof Of Proposition 1: Sincementioning

confidence: 99%

“…It selects samples of rows and columns of the population matrix independently and considers only the population units in the rows and the columns that have been selected. This design is considered in Vos [22], Skinner [17], and Juillard et al [13]. This work investigates a two dimensional population matrix where entry (i, j) of the matrix has a single unit.…”

Section: Introductionmentioning

confidence: 99%

Sampling a two dimensional matrix

Rivest

Ebouele

2020

Computational Statistics & Data Analysis

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A new sampling design for populations whose units can be arranged as an N ×M matrix is proposed. The sample must satisfy some constraints: row and column sample sizes are set in advance. The proposed sampling method gives the same selection probability to all the sample matrices that satisfy the constraints.Three algorithms to select a sample uniformly in the feasible set are presented: an exact algorithm based on the multivariate hypergeometric distribution, an MCMC algorithm, and the cube method. Their performances are evaluated using Monte Carlo simulations. The designs for sampling elements in a given row or a given column are investigated and the single inclusion and joint selection probabilities under the proposed design are evaluated. Several variance estimators are proposed for the Horvitz-Thompson estimator of the population mean of the survey variable y and their performances are compared in a Monte Carlo study. A numerical example dealing with a creel survey of fishermen found at 9 sites over 36 days is presented.

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Sampling in Space and Time

Cited by 8 publications

References 1 publication

Sampling minority groups in health surveys

Sampling minority groups in health surveys

Cross-classified sampling: Some estimation theory

Sampling a two dimensional matrix

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