多帧测量的准随机重采样设计

IF 1.6 Q1 STATISTICS & PROBABILITY Statistica Pub Date : 2019-11-06 DOI:10.6092/ISSN.1973-2201/8930
Cherif Ahmat Tidiane Aidara
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引用次数: 2

摘要

在本文中,我们提出了两种新的算法,它们使用混洗的Sobol序列来生成多帧调查中的bootstrap重采样设计。我们在使用三个重叠帧设置设计的仿真研究中研究了所提出的算法的性能。使用分层简单随机抽样设计从帧中独立地选择样本。在相对百分比偏差、变异系数和95%置信区间的经验覆盖概率方面,所提出的方法的性能与已经建立的方法(如用于多框架调查的Lohr Rao bootstrap方法)相当。
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Quasi Random Resampling Designs for Multiple Frame Surveys
In this paper, we present two new algorithms that use the shuffled Sobol sequence to generate the bootstrap resampling designs in multiple frame surveys. We investigate the performance of the proposed algorithms in a simulation study using a three-overlapping frame setup design. The samples were selected independently from the frames using a stratified simple random sampling design. The performance of the proposed methods is comparable with the already established ones such as the Lohr-Rao bootstrap methods for multiple frame surveys in terms of relative percentage bias, coefficient of variation, and empirical coverage probabilities of 95 percent confidence interval.
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来源期刊
Statistica
Statistica STATISTICS & PROBABILITY-
CiteScore
1.70
自引率
0.00%
发文量
0
审稿时长
10 weeks
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