On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling

Beaumont, Jean-François; Patak, Zdenek

doi:10.1111/j.1751-5823.2011.00166.x

Cited by 41 publications

(34 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Replication variance estimation techniques, such as the bootstrap, may provide a practical way of obtaining v P (θ RHT ) and v P (θ RHT −θ HT ). Assuming c is fixed, there are a number of methods that can be used and that are typically implemented by generating bootstrap weights; e.g., Rao et al (1992) and Beaumont & Patak (2012).…”

Section: Mean Square Error Estimationmentioning

confidence: 99%

A unified approach to robust estimation in finite population sampling

2013

View full text Add to dashboard Cite

We argue that the conditional bias associated with a sample unit can be a useful measure of influence in finite population sampling. We use the conditional bias to derive robust estimators that are obtained by downweighting the most influential sample units. Under the model-based approach to inference, our proposed robust estimator is closely related to the well-known estimator of Chambers (1986). Under the design-based approach, it possesses the desirable feature of being applicable with most sampling designs used in practice. For stratified simple random sampling, it is essentially equivalent to the estimator of Kokic & Bell (1994). The proposed robust estimator depends on a tuning constant. In this paper, we propose a method for determining the tuning constant and show that the resulting estimator is consistent. Results from a simulation study suggest that our approach improves the efficiency of standard nonrobust estimators when the population contains units that may be influential if selected in the sample.

show abstract

Section: Mean Square Error Estimationmentioning

confidence: 99%

A unified approach to robust estimation in finite population sampling

2013

View full text Add to dashboard Cite

show abstract

“…The use of bootstrap techniques in survey sampling has been widely studied in the literature. Most of them may be thought as particular cases of the weighted bootstrap [1][2][3]; see also [10,23,38] and [11] for detailed reviews.…”

Section: 2mentioning

confidence: 99%

Coupling methods for multistage sampling

Chauvet¹

2015

Ann. Statist.

View full text Add to dashboard Cite

Multistage sampling is commonly used for household surveys when there exists no sampling frame, or when the population is scattered over a wide area. Multistage sampling usually introduces a complex dependence in the selection of the final units, which makes asymptotic results quite difficult to prove. In this work, we consider multistage sampling with simple random without replacement sampling at the first stage, and with an arbitrary sampling design for further stages. We consider coupling methods to link this sampling design to sampling designs where the primary sampling units are selected independently. We first generalize a method introduced by [Magyar Tud. Akad. Mat. Kutat\'{o} Int. K\"{o}zl. 5 (1960) 361-374] to get a coupling with multistage sampling and Bernoulli sampling at the first stage, which leads to a central limit theorem for the Horvitz--Thompson estimator. We then introduce a new coupling method with multistage sampling and simple random with replacement sampling at the first stage. When the first-stage sampling fraction tends to zero, this method is used to prove consistency of a with-replacement bootstrap for simple random without replacement sampling at the first stage, and consistency of bootstrap variance estimators for smooth functions of totals.Comment: Published at http://dx.doi.org/10.1214/15-AOS1348 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

show abstract

“…This approach is pursued in [27], [32], where the re-sampled data produced by the "usual" i.i.d. bootstrap are properly rescaled, as well as in [34], [2], [10], [14], where a "rescaled bootstrap process" based on asymptotic results is proposed. Among the ad hoc approaches we also quote the recent paper by [1], where an ingenious mixed resampling design is proposed to account for the dependence among observations.…”

Section: Introductionmentioning

confidence: 99%

A unified principled framework for resampling based on pseudo-populations: Asymptotic theory

Conti¹,

Marella²,

Mecatti³

et al. 2020

Bernoulli

View full text Add to dashboard Cite

In this paper, a class of resampling techniques for finite populations under complex sampling design is introduced. The basic idea on which they rest is a two-step procedure consisting in:(i) constructing a "pseudo-population" on the basis of sample data; (ii) drawing a sample from the predicted population according to an appropriate resampling design. From a logical point of view, this approach is essentially based on the plug-in principle by Efron, at the "sampling design level". Theoretical justifications based on large sample theory are provided. New approaches to construct pseudo populations based on various forms of calibrations are proposed. Finally, a simulation study is performed.

show abstract

On the Generalized Bootstrap for Sample Surveys with Special Attention to Poisson Sampling

Cited by 41 publications

References 14 publications

A unified approach to robust estimation in finite population sampling

A unified approach to robust estimation in finite population sampling

Coupling methods for multistage sampling

A unified principled framework for resampling based on pseudo-populations: Asymptotic theory

Contact Info

Product

Resources

About