Bootstrap Methods for Judgment Post Stratification

Mozhgan Alirezaei Dizicheh, Nasrollah Iranpanah and Ehsan Zamanzade ()
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Mozhgan Alirezaei Dizicheh: University of Isfahan
Nasrollah Iranpanah: University of Isfahan
Ehsan Zamanzade: University of Isfahan

Statistical Papers, 2021, vol. 62, issue 5, No 18, 2453-2471

Abstract: Abstract It has been shown in the literature that judgment post stratification (JPS) sampling design often leads to more efficient statistical inference than what is possible to obtain in simple random sampling (SRS) design of comparable size. Since the JPS is a cost-efficient sampling design, a large enough sample size may not be available to use normal theory of the estimators. In this paper, we describe two bootstrap methods for JPS sampling scheme, one of which has been already used in the literature without studying its consistency and the other is new. We also show that both bootstrap approaches are consistent. We then investigate the use of the bootstrap methods for constructing confidence intervals for the population mean and compare them with the confidence interval of the population mean obtained via normal approximation (NA) method using Monte Carlo simulation. It is found that for the asymmetric distributions, one of the bootstrap methods we describe in the paper often leads to a closer coverage probability (CP) to the nominal level than NA method. Finally, a real dataset is analysed for illustration.

Keywords: Confidence interval; Coverage probability; Judgment post stratified sample; Resampling methods; Simple random sample; 62G09; 62F40; 62D05 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s00362-020-01197-x

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