Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey

Faweya Olanrewaju; Amahia Godwin Nwanzu; Adeniran Adefemi Tajudeen

doi:doi:10.11648/j.sjams.20170502.12

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Estimation of Missing Data Using Convoluted Weighted Method in Nigeria Household Survey

Faweya Olanrewaju, Amahia Godwin Nwanzu, Adeniran Adefemi Tajudeen

Published in Science Journal of Applied Mathematics and Statistics (Volume 5, Issue 2)

Received: 4 October 2016 Accepted: 12 October 2016 Published: 10 March 2017

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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.

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), 2017. Published by Science Publishing Group

Keywords

Missing Data, MCAR, Stein Rule, Least Squares, Convoluted Weighted Method

References

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[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).
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[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.
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[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.
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[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).
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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

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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

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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

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@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}
}

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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  -

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Author Information

Faweya Olanrewaju

Department of Statistics, Ekiti State University, Ado Ekiti, Nigeria
Amahia Godwin Nwanzu

Department of Statistics, University of Ibadan, Ibadan, Nigeria
Adeniran Adefemi Tajudeen

Department of Statistics, University of Ibadan, Ibadan, Nigeria

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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

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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

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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

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}
}

Copy | Download

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  -

Copy | Download