Nuanpan Lawson
ABSTRACT
A new method to estimate the variance of population total with free joint inclusion probability which is usually unknown in practice has been proposed under unequal probability sampling without replacement when nonresponse occurs in this study. Considered under a reverse framework where the sampling fraction is negligible and the response probabilities for all units are all equal. A simulation study is employed to see the performance of the new estimator compared to existing estimators.
- Hansen, M. H. and Hurwitz, W. N. 1946. The problem of non-response in sample surveys. J. Am. Stat. Assoc., 41, 517--529. DOI = https://doi.org/10.2307/2280572Google Scholar
Cross Ref
- Deville, J. C. and Särndal, C. E. 1994. Variance estimation for the regression imputed Horvitz Thomson estimator. Journal of Official Statistics, pp. 33--40. DOI = https://doi.org/10.111/insr.121972Google Scholar
- Shao, J. and Steel, P. 1999. Variance estimation for survey data with composite imputation and nonnegligible sampling fractions. Journal of the American Statistician Association, pp. 254--265. DOI=https://doi.org/10.2307/2669700Google ScholarCross Ref
- Berger, Y.G. 2003. A simple variance estimator for unequal probability sampling without replacement. Journal of Applied Statistics, 31, 305--315. DOI= https://doi.org/10.1080/ 02666042000184046Google ScholarCross Ref
- Särndal, C.E. and Lundström, S. 2005. Estimation in surveys with nonresponse. John Wiley & Sons, Chichester.Google Scholar
- Haziza, D. 2010. Resampling methods for variance estimation in the presence of missing survey data. Proceedings of the Annual Conference of the Italian Statistical Society.Google Scholar
- Lawson, N. 2017. Variance estimation in the presence of nonresponse under probability proportional to size sampling, Proceeding of the 6th Annual International Conference on Computational Mathematics, Computational Geometry and Statistics 2017 (CMCGS 2017), 6th-7th March 2017, Singapore. DOI=https://doi.org/10.5176/2251-1911_CMCGS17.32Google ScholarCross Ref
- Horvitz, D.F. and Thompson, D.J. 1952. A generalization of sampling without replacement from a finite universe. Journal of the America Statistical Association, 260, 663--685.Google ScholarCross Ref
- Hájek, J. 1964. Asymptotic theory of rejective sampling with varying probabilities from a finite population. Ann. Math. Statist. 35, 1491--1523.Google ScholarCross Ref
- Ponkaew, C. and Lawson, N. 2019. Estimating variance in the presence of nonresponse under unequal probability sampling without replacement. Suranaree Journal of Science and Technology. (In press)Google Scholar
- Fay, R. E. 1991. A design-based perspective on missing data variance. Proceedings of the 1991 Annual Research Conference, US Bureau of the Census, pp. 440--429.Google Scholar
- Midzuno, H. 1952. On sampling system with probability proportional to sum of sizes, Ann. Inst.Statist. Math. 99--107.Google Scholar
Index Terms
- Variance Estimation in the Presence of Nonresponse with Free Joint Inclusion Probability under Unequal Probability Sampling without Replacement
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