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New Generalized Regression Estimators Using a Ratio Method and Its Variance Estimation for Unequal Probability Sampling without Replacement in the Presence of Nonresponse

New Generalized Regression Estimators Using a Ratio Method and Its Variance Estimation for Unequal Probability Sampling without Replacement in the Presence of Nonresponse

Original Research ArticleAug 15, 2022Vol. 23 No. 2 (2023) 10.55003/cast.2022.02.23.007

Abstract

One of the most important problems for planning in economics is that reliable data can be difficult to obtain either because it has not been recorded or because of nonresponse in surveys. This paper is aimed at proposing new generalized regression estimators using the ratio method of estimation for estimating population mean and population total and also variance estimators of the proposed generalized regression estimators in the presence of uniform nonresponse of a study variable. We show in theory that the proposed estimators are almost unbiased under unequal probability sampling without replacement when nonresponse occurs in the study. In the simulation studies, the performances of the proposed estimators were better when compared to the existing ones in terms of minimum relative bias and relative root mean square error. In an application to Thai maize in Thailand with 2019 data, we can see that the proposed estimators gave smaller variance estimates when compared to the existing estimators.

Keywords: ratio estimator; generalized regression estimator; automated linearization approach; response probabilities; nonresponse

*Corresponding author: Tel.: (+66) 25552000 ext.  4903

                                             E-mail: nuanpan.n@sci.kmutnb.ac.th

References

1
Hansen, M.H. and Hurwitz, W.N., 1946. The problem of nonresponse in sample surveys. Journal of the American Statistical Association, 41, 517-529.
2
Särndal, C.E. and Lundström, S., 2005. Estimation in Surveys with Nonresponse. New York: John Wiley.
3
Horvitz, D.F. and Thompson, D.J., 1952. A generalization of sampling without replacement from a finite universe. Journal of the American Statistical Association, 47(260), 663-685.
4
Estevao, V.M. and Särndal, C.E., 2006. Survey estimates by calibration on complex auxiliary information. International Statistical Review, 72(2), 127-147.
5
Chauvet, G., 2016. Variance estimation for the 2006 French housing survey. Mathematical Population Studies, 23(3), 147-163.

Author Information

Chugiat Ponkaew

Department of Mathematics, Faculty of Science and Technology, Phetchabun Rajabhat University, Phetchabun, Thailand

Nuanpan Lawson*

Department of Applied Statistics, Faculty of Applied Science, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

About this Article

Current Journal

Vol. 23 No. 2 (2023)

Type of Manuscript

Original Research Article

Keywords

ratio estimator;
generalized regression estimator;
automated linearization approach;
response probabilities;
nonresponse

Published

15 August 2022

DOI

10.55003/cast.2022.02.23.007

Current Journal

Journal Cover
Vol. 23 No. 2 (2023)

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