Quasi Random Resampling Designs for Multiple Frame Surveys
DOI:
https://doi.org/10.6092/issn.1973-2201/8930Keywords:
Multiple frame surveys, Bootstrap variance method, Shuffled Sobol sequenceAbstract
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.
References
C. A. T. AIDARA (2013). Bootstrap variance estimation for complex survey data: A quasi Monte Carlo approach. Sankhya B, 75, no. 1, pp. 29–41.
I. ANTONOV, V. SALEEV (1979). An economic method of computing LP-sequences. USSR Computational Mathematics and Mathematical Physics, 19, pp. 252–256.
J. CHEN, R. SITTER (1999). A pseudo empirical likelihood approach to the effective use of auxiliary information in complex surveys. Statistica Sinica, 9, pp. 385–406.
D. CHRISTOPHE, S. PETR (2015). Randtoolbox: Generating and Testing Random Numbers. R package version 1.17.
D. EDDELBUETTEL, J. J. BALAMUTA (2017). Extending R with C++: A Brief Introduction to Rcpp. PeerJ Preprints, 5:e3188v1. URL https://doi.org/10.7287/peerj.preprints.3188v1.
C. GIRARD (2009). The Rao-Wu rescaling bootstrap: From theory to practice. In Proceedings of the Federal Committee on Statistical Methodology Research Conference. Federal Committee on Statistical Methodology,Washington, DC, pp. 2–4.
J. HALTON (1960). On the efficiency of certain quasi-random sequences of points in evaluating multi-dimensional integrals. Numerische Mathematik, 2, pp. 84–90.
H. HARTLEY (1962). Multiple frame surveys. In Proceedings of the Social Statistics Section.
S. JOE, F. Y. KUO (2008). Notes on generating Sobol sequences. URL https://web.maths.unsw.edu.au/ fkuo/sobol/joe-kuo-notes.pdf. Available online.
G. KALTON, D. ANDERSON (1986). Sampling rare populations. Journal of the Royal Statistical Society, Series A, 149, no. 1, pp. 65–82.
S. LOHR (2007). Recent developments in multiple frame surveys. Journal of the American Statistical Association, pp. 3257–3264.
S. LOHR, J. RAO (2000). Inference from dual frame surveys. Journal of the American Statistical Association, 95, no. 449, pp. 271–280.
S. LOHR, J. RAO (2006). Estimation in multiple- frame surveys. Journal of the American Statistical Association, 101, no. 475, pp. 1019–1030.
F. MECATTI (2007). A single frame multiplicity estimator for multiple frame surveys. Component of Statistics Canada, Catalogue n0.12-001-X, Business Survey Methods Division.
R CORE TEAM (2018). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
C. SKINNER, J. RAO (1996). Estimation in dual frame surveys with complex design. American Statistical Association, 91, no. 433, pp. 349–356.
O. TEYTAUD, S. GELLY, S. LALLICH, E. PRUDHOMME (2006). Quasi-random resamplings, with applications to rule extraction, cross-validation and (su-)bagging. Dans International Workshop on Intelligent Information Access III A 2006.
Y. TILLÉ, A. MATEI (2016). Sampling: Survey Sampling. URL https://CRAN.R-project.org/package=sampling. R package version 2.8.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2019 Statistica
This journal is licensed under a Creative Commons Attribution 3.0 Unported License (full legal code).
Authors accept to transfer their copyrights to the journal.
See also our Open Access Policy.
