PERFORMANCE EVALLUATION OF IMPUTATION-BASED ESTIMATORS FOR NON-RESPONSE AND MEASUREMENT ERROR CHALLENGES

Authors

Keywords:

Efficiency, Non-response, Simulation, Estimation, Imputation

Abstract

This study aims to develop a robust class of estimators designed to address non-response and measurement erros, which frequently complicate data collection in mdeical and social science research. By employing call-back and imputation schemes, the proposed estimators enhance efficiency and accuracy. We derived properties such as bias and mean squared error using Taylor's series explansion and testes their consistenct. An empirical study with simulated data rfom vaious distributions revealed that the proposed estimators outperform existing ones. Thus, these modified classes are recommended for practical application in data analysis, especially in the presence of non-reponse and measurement errors.

Dimensions

Allen, J, Singh, H.P & Smarandache, F. (2003). A family of estimators of Population mean multi-auxiliary Information in presence of Measurement errors. International Journal of Social Economic, 30 (7), 837-849.

Al-Omari, A.I., Bouza, C.N., Herrera, C. (2013). Imputation Methods of Missing data for estimating the population mean using simple random sampling with known correlation coefficient. Quality & Quantity 47(1) 353-65.

Audu, A., Ishaq, O.O Mulli, J.O., Abukabar, A., Rashida, A., Akintola, A & Isah, U. (2020). Modified Estimation of Population Mean using Robust Multiple Regression.Methods. NIPES Journal of Science and Technology Research 2(4), 2682-5821

Bhushan S. and Pandey A.P. (2016). Optimal imputation of missing data for estimation of population mean. Journal of Statistics and Management Systems.19, 755-769

Cochran, W.G (1968). Error of Measurement in Statistics. Teachnometrics, 10(4), 637-666.

Diana, G., & Perri, P.F. (2010). Improved Estimators of the Population Mean for missing data. Communications in Statistic: theory and method 39 (18), 3245-3251.

Gira, A.A (2015). Estimation of Population mean with a new Imputation methods. Applied Mathematics Science 9 (34), 1663

Hansen, M.H & Hurwitz, W.N (1946). The Problem of non-response in sample survey. Journal of the American Statistical Association, 41(236), 517-529.

Kadlar, C.& Cingi, H. (2008). Estimators for the Population mean in the case of missing data Communications in statistics. Theory and methods,37 (914).

Maneesha S. and Singh R.K. (2002). Role of Regression Estimator Involving Measurement Errors. Brazilian Journal of Probability and Statistics,16, 39-46.

Muneer, S., Shabbir, J., & Khalil, A. (2018). A generalized exponential type estimator of population mean in the presence of non-response. Statistics in Transition New Series, 19(2).

Salim, N. M., & Onyango, C. O. (2022). A Modified Regression Estimator for Single Phase Sampling in the Presence of Observational Errors. Open journal of Statistics, 12(2), 175-187. https://doi.org/10.4236/ojs.2022.122012.

Shalabh, & Tsai, J.R (2017) Ratio and product Method of Estimation of Population mean in the Presence of Correlated measurement errors, Communications in Satistics Simulation and Computation, 46(7), 5566-5593.

Singh, S. Horn, S. (2000) Compromised Imputation in survey sampling. Metrika 51 (3), 267-76

Singh, S. & Deo, B. (2003). Imputation by Power transformation. Statistical paper 44 (4), 555-79

Singh, S. (2009): A new method of imputation in survey sampling, Statistics: A Journal of Theoretical and Applied Statistics, 43:5, 499511.

Singh, G.N., Priyanka, K., Kim, J.M., Singh, S. (2010). Estimation of population mean using imputation techniques in sample survey. Journal of Korean Statistics Society 39(1): 67-74.

Singh, G.N.,Maurya, S., Khetan, M. and Kadilar, C. (2016). Some imputation methods for missing data in sample surveys. Haceeltepe journal of Mathematics and Statistics 45(6), 1865-80.

Singh, B.K., & Gogoi, U. (2017). Estimation of Population Mean using exponential dual to ratio type compromised imputation for missing data in survey sampling. J Appl pro, 3, 515-522.

Singh, P., Singh, R., & Bouza, C. N. (2018). Effect of measurement error and non-response on estimation of population mean. Investigacin Operacional, 39(1). https://revistas.uh.cu/invoperacional/article/view/4082

Singh, N., Vishakarma, K.G., Gangele, R.K, (2021). Variance Estimation in the Presence of Measurement Errors under stratified Random sampling. Statistical Journal, 19 (2), 275-290.

Toutenburg, H., Strivastava V.K., and Shalabh, A (2008). Imputation of missing values through ratio method in sample survey. Statistical papers 49: 237-47.

Wang, L. and Wang, Q. (2006). Empirical likelihood for parametric model under imputation for missing data. Journal of Management Systems, 9(1), 1-13.

Zaman, T., Dunder, E., Audu, A., Ailah, D.A., Shahzad, U. & Hanif, M. (2021). Robust Regression Ratio-Type Estimators of the mean Utilizing Two Auxiliary Variables: A simulation Study. Mathematical Problems in Engineering,1- 9 .

Published

14-12-2024

How to Cite

PERFORMANCE EVALLUATION OF IMPUTATION-BASED ESTIMATORS FOR NON-RESPONSE AND MEASUREMENT ERROR CHALLENGES. (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 299-305. https://doi.org/10.33003/fjs-2024-0806-2986

Issue

Vol. 8 No. 6 (2024): FUDMA Journal of Sciences - Vol. 8 No. 6

Section

Research Articles
Copyright & Licensing

FUDMA Journal of Sciences

How to Cite

PERFORMANCE EVALLUATION OF IMPUTATION-BASED ESTIMATORS FOR NON-RESPONSE AND MEASUREMENT ERROR CHALLENGES. (2024). FUDMA JOURNAL OF SCIENCES, 8(6), 299-305. https://doi.org/10.33003/fjs-2024-0806-2986