Cross-classified sampling: Some estimation theory

Skinner, Chris J.

doi:10.1016/j.spl.2015.06.001

Cited by 3 publications

(6 citation statements)

References 9 publications

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“…Despite the present results and the recent paper by Skinner (2015), the crossclassified sampling design still deserves some attention. In particular, the treatment of non-response and the calibration problem should also be taken into account, and is currently under investigation.…”

Section: Resultscontrasting

confidence: 88%

“…Due to its particular properties, CCS deserves a specific attention. However, as noted by Skinner (2015), "the literature on the theory of cross-classified sampling is very limited". In particular, no general theory is derived under the finite population framework.…”

Section: Introductionmentioning

confidence: 99%

“…This sampling design appears in other contexts than the ELFE survey. Some examples include consumer price index surveys, as detailed in Dalén & Olhsson (1995) for the Swedish survey, where outlets and items are sampled, and business surveys (Skinner, 2015), where businesses and products are sampled. Due to its particular properties, CCS deserves a specific attention.…”

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confidence: 99%

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Estimation Under Cross-Classified Sampling With Application to a Childhood Survey

Juillard

Chauvet

Ruiz‐Gazen

2017

Journal of the American Statistical Association

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International audienceThe cross-classified sampling design consists in drawing samples from a two-dimensional population, independently in each dimension. Such design is commonly used in consumer price index surveys and has been recently applied to draw a sample of babies in the French Longitudinal Survey on Childhood, by crossing a sample of maternity units and a sample of days. We propose to derive a general theory of estimation for this sampling design. We consider the Horvitz–Thompson estimator for a total, and show that the cross-classified design will usually result in a loss of efficiency as compared to the widespread two-stage design. We obtain the asymptotic distribution of the Horvitz–Thompson estimator and several unbiased variance estimators. Facing the problem of possibly negative values, we propose simplified nonnegative variance estimators and study their bias under a super-population model. The proposed estimators are compared for totals and ratios on simulated data. An application on real data from the French Longitudinal Survey on Childhood is also presented, and we make some recommendations. Supplementary materials for this article are available online

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Section: Resultscontrasting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Estimation Under Cross-Classified Sampling With Application to a Childhood Survey

Juillard

Chauvet

Ruiz‐Gazen

2017

Journal of the American Statistical Association

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“…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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“…Their equations (3.3)-(3.6) give the form of this estimator, which contains many terms and is not recapitulated here. They deal only with the variation in the prices, with further thinking about how the variation in the weights should be incorporated inDalén (1995) Skinner (2015). extends the cross-classified sampling results and in particular gives a bootstrap estimator (see Section 3.3), which is computationally easier, although it has not yet been applied to price indices.…”

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confidence: 99%

Estimating Sampling Errors in Consumer Price Indices

Smith

2021

Int Statistical Rev

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Several different approaches have been proposed for the calculation of sampling errors in consumer price indices (CPIs), but only one is in regular use providing published estimates. The history of the development of sampling error estimation for CPIs is reviewed, and the development in the USA (where most research has taken place) is summarised. The proposed approaches for sampling error estimation are set out, with their properties and their different conceptualisations of the sampling error. The frameworks for the measurement of errors more widely in the CPI are also reviewed, and the evidence for the importance of the sampling error among the other error sources is presented. Some opportunities and directions for further work are provided.

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Cross-classified sampling: Some estimation theory

Abstract: For a population represented as a two-way array, we consider sampling via the product of independent row and column samples. Theory is presented for the estimation of a population total under alternative methods of sampling the rows and columns.

Cited by 3 publications

References 9 publications

Estimation Under Cross-Classified Sampling With Application to a Childhood Survey

Estimation Under Cross-Classified Sampling With Application to a Childhood Survey

Sampling a two dimensional matrix

Estimating Sampling Errors in Consumer Price Indices

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