The analysis of survey data becomes difficult in the presence of missing data. By the use of Least Squares and Stein Rule method, estimator for the parameters of interest can be obtained. In this study, proposed convoluted Weighted Least Squares and Stein Rule method is compared with some existing techniques where the data is considered missing completely at random (MCAR). The results show that other techniques are occasionally useful in estimating most of the parameter, but proposed (LSSR) technique perform better regardless of the percentage of the missing data under MCAR assumption.
| Published in | Science Journal of Applied Mathematics and Statistics (Volume 5, Issue 2) |
| DOI | 10.11648/j.sjams.20170502.12 |
| Page(s) | 70-77 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Missing Data, MCAR, Stein Rule, Least Squares, Convoluted Weighted Method
| [1] | Amahia, G. N (2010) Factors, Prevention and Correction Methods for Non-Response in Sample Surveys. Central Bank of Nigeria, Journal of Applied Statistics 1 (1), pp 79-89, (Nigeria). |
| [2] | Arbuckle, J. L. (1996). Full Information Likelihood Estimation in the Presence of Incomplete Data. |
| [3] | Beale, E. M. L., & Little, R. J. A (1975). Missing Values in Multivariate Analysis. Journal of the Royal Statistical Society, Series B, 37, 129-146. |
| [4] | Cochran, W. (1968). "The Effective of Adjustment by Sub Classification in Removing Bias in Observational Studies." Biometrics, 24, pp. 295-313. |
| [5] | Cool, A. L. (2000). A Review of Methods for Dealing with Missing Data. Paper presented at the Annual Meeting of the Southwest Educational Research Association, Dallas, TX. (Eric Document Reproduction Service No. ED 438 311). |
| [6] | Diggle P. J Testing for Random Dropouts in Repeated Measurement Data. Biometrics, 45 (1989) 1255-1258. |
| [7] | Diggle P. J., Heagerty P, Liang, K. Y. et al. Analysis of Longitudinal Data. 2nd ed. Oxford University Press Inc., (2002). |
| [8] | Dixon W. J BMDP Statistical software. Los Angeles. University of California Press, (1988). |
| [9] | Ender C. K., and Bandalos, D. L (2001). The Relative Performance of Full Information Maximum Likelihood Estimation for Missing Data in Structural Equation Models. Structural Equation Modeling, 8(3), 430-457. |
| [10] | Groves, R Couper M. (1998); Non-response Interview Survey, New York Wiley. |
| [11] | Groves, R Singer. and Corning, A (2000); Leverage- Salience Theory of Survey Participation Description and an Illustration. Public Opinion Quarterly, 64, 299-308. |
| [12] | Groves, R. Cialdini R and Couper M (1992): “Understanding the Decision to participate in a Survey” The public Opinion Quaterly, 54 (4), 475-495. |
| [13] | Howell, D. C (2007) The Analysis of Missing Data. Handbook of social science Methodology London: Sage. Return. |
| [14] | Kalton, G. and Flores-Cervantes, I (2003), “Weighting Methods” Journal of Official statistics, 19, pp. 81-97. |
| [15] | Kim, J. O., & Curry, J. (1977). The Instrument of Missing Data in Multivariate Analysis Sociological Methods & Research, 6 (2), 215-240. |
| [16] | Little R. J. A. A Test of Missing Completely at Random for Multivariate Data with Missing Values. Journal of American Statistical Association, 83 (1988), 1198-2002. |
| [17] | Madlow, W. G., Nisselson, H., Olkin, I. (Eds.), 1983. Incomplete Data in Sample Surveys. Report and Case Studies, vol.1. Academic Press, New York. |
| [18] | Rao and Toutenburg. H (1999) Linear Models: Least Square and Alternatives. New York: Springer-Verlag. |
| [19] | Quinten, A., Raaijmakers, W., 1999. Effectiveness of Different Missing Data Treatments in Survey with Likert-Type Data: Introducing the Relative Mean Substitution Approach. Educational and Psychological Measurement 59 (5), 725-748. |
| [20] | Schafer J. L., Graham J. W. Missing Data: Over View of the State of the Art. Psychological Methods, 7 (202), 147: 177. |
| [21] | Schafer, J (2002). Dealing with Missing Data. Research Letter Information Mathematics Science. Vol. 3, pp 153-160. |
| [22] | Tshering, S., Okazaki T and Endo S., March 2013: A Method to Identify Missing Data Mechanism in Incomplete Dataset. IJCSNS International Journal of Computer Science and Network Security, Vol. 13 No14 Page (14-21). |
| [23] | Utazi C. E., Onyeagu S. I. & Osuji G. A (2010). On the Efficiency of Some Techniques for Estimating Covariance and Correlation Matrices from Incomplete Data. Journal of the Nigerian Statistical Association Vol. 22, 44-63. |
APA Style
Faweya Olanrewaju, Amahia Godwin Nwanzu, Adeniran Adefemi Tajudeen. (2017). Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey. Science Journal of Applied Mathematics and Statistics, 5(2), 70-77. https://doi.org/10.11648/j.sjams.20170502.12
ACS Style
Faweya Olanrewaju; Amahia Godwin Nwanzu; Adeniran Adefemi Tajudeen. Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey. Sci. J. Appl. Math. Stat. 2017, 5(2), 70-77. doi: 10.11648/j.sjams.20170502.12
AMA Style
Faweya Olanrewaju, Amahia Godwin Nwanzu, Adeniran Adefemi Tajudeen. Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey. Sci J Appl Math Stat. 2017;5(2):70-77. doi: 10.11648/j.sjams.20170502.12
Share This Article
Twitter
Linked In
Facebook
Copy
Download@article{10.11648/j.sjams.20170502.12,
author = {Faweya Olanrewaju and Amahia Godwin Nwanzu and Adeniran Adefemi Tajudeen},
title = {Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey},
journal = {Science Journal of Applied Mathematics and Statistics},
volume = {5},
number = {2},
pages = {70-77},
doi = {10.11648/j.sjams.20170502.12},
url = {https://doi.org/10.11648/j.sjams.20170502.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjams.20170502.12},
abstract = {The analysis of survey data becomes difficult in the presence of missing data. By the use of Least Squares and Stein Rule method, estimator for the parameters of interest can be obtained. In this study, proposed convoluted Weighted Least Squares and Stein Rule method is compared with some existing techniques where the data is considered missing completely at random (MCAR). The results show that other techniques are occasionally useful in estimating most of the parameter, but proposed (LSSR) technique perform better regardless of the percentage of the missing data under MCAR assumption.},
year = {2017}
}
TY - JOUR T1 - Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey AU - Faweya Olanrewaju AU - Amahia Godwin Nwanzu AU - Adeniran Adefemi Tajudeen Y1 - 2017/03/10 PY - 2017 N1 - https://doi.org/10.11648/j.sjams.20170502.12 DO - 10.11648/j.sjams.20170502.12 T2 - Science Journal of Applied Mathematics and Statistics JF - Science Journal of Applied Mathematics and Statistics JO - Science Journal of Applied Mathematics and Statistics SP - 70 EP - 77 PB - Science Publishing Group SN - 2376-9513 UR - https://doi.org/10.11648/j.sjams.20170502.12 AB - The analysis of survey data becomes difficult in the presence of missing data. By the use of Least Squares and Stein Rule method, estimator for the parameters of interest can be obtained. In this study, proposed convoluted Weighted Least Squares and Stein Rule method is compared with some existing techniques where the data is considered missing completely at random (MCAR). The results show that other techniques are occasionally useful in estimating most of the parameter, but proposed (LSSR) technique perform better regardless of the percentage of the missing data under MCAR assumption. VL - 5 IS - 2 ER -
Information
Email: