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Nduka, E. C.
- Methods of Analysing Missing Values in a Regression Model
Abstract Views :411 |
PDF Views:94
Authors
Affiliations
1 Department of Mathematics and Statistics University of Port Harcourt, NG
2 Department of Mathematics and Statistics University of Port Harcourt
1 Department of Mathematics and Statistics University of Port Harcourt, NG
2 Department of Mathematics and Statistics University of Port Harcourt
Source
Indian Journal of Science and Technology, Vol 5, No 2 (2012), Pagination: 2013-2016Abstract
Different methods of imputation are adopted in this study to compensate for missing values encountered in the data collected. The imputation methods considered are the overall mean value, Random Overall, Logistic Regression, Linear Regression, Predictive Match, Multiple Imputations and the Hot Deck Imputation. The various values obtained by the methods were analysed and compared using Bartlett's test statistic for equality of variances among groups (Mean Square Errors of the seven methods). The software packages used for this research work are Winmice, Solas and SAS. Different values were estimated applying the various methods. However, results obtained from the test showed that the variances among the groups have no significant differences, that is, any of the imputation methods could be used. Further test using relative variance revealed that the multiple imputation method may be preferred.Keywords
Missing at Random, Imputation, Bartlett's Test, Coefficient of Relative VarianceFull Text
References
- Afifi AA and Elashoff RM (1966) Missing observations in multivariate statistics. J. Am. Stat. Asso. 61(2), 595–604.
- Kim J and Curry J (1977) The Treatment of Missing Data in Multivariate Analysis. Sociol. Methods & Res. 6, 215–240.
- Lepkowski JM, J Richard Landis, Sharon A Stehouwer (1987) Strategies for Analyzing of Imputed Data from Sample Survey. The Med. Care utilization & Expenditure. 25(8), 705-715.
- Little RJA and Rubin DB (2002) Statistical analysis with missing data 2nd Edition, Wiley, NY.
- Rubin DB (1986) Statistical matching using file concentration with adjusted weights and multiple imputation. J. Bus. & Econ. Stat. 4, 87-94.
- SAS Learning Edition Version 4.1. Bringing the power of data analytics to individuals. http://www.amazon. com/SAS-Learning-4-1-Little-nterprise/dp/1590479173.
- Methods of Analysing Missing Values in a Regression Model
Abstract Views :301 |
PDF Views:88
Authors
U. P. Ogoke
1,
E. C. Nduka
1
Affiliations
1 Department of Mathematics and Statistics, University of Port Harcourt, Rivers State, NG
1 Department of Mathematics and Statistics, University of Port Harcourt, Rivers State, NG
Source
Indian Journal of Science and Technology, Vol 5, No 1 (2012), Pagination: 1831-1833Abstract
Different methods of imputation are adopted in this study to compensate for missing values encountered in the data collected. The imputation methods considered are the Overall Mean Value, Random Overall, Logistic Regression, Linear Regression, Predictive Match, Multiple Imputations and the Hot Deck Imputation. The various values obtained by the methods were analysed and compared using Bartlett's test statistic for equality of variances among groups (Mean Square Errors of the seven methods).The software packages used for this research work are Winmice, Solas and SAS (Winmice Prototype Version 0.1; Solas Version 3.2.; SAS Learning Edition Version 4.1). Different values were estimated applying the various methods. However, results obtained from the test showed that the variances among the groups have no significant differences, that is, any of the imputation methods could be used. Further test using relative variance revealed that the multiple imputation method may be preferred.Keywords
Missing at Random, Imputation, Bartlett's Test, Coefficient of Relative VarianceFull Text
References
- Afifi AA and Elashoff RM (1966) Missing observations in multivariate statistics. J.Am. Stat. Assoc. 61(2), 595– 604
- Kim, J. and Curry, J. (1977) The treatment of missing data in multivariate analysis. Sociol. Methods & Res. 6(Nov), 215–240.
- Lepkowski, J. M, Richard Landis J and Sharon A Stehouwer (1987) Strategies for analyzing of Imputed data from sample survey. In: Medical Care utilization & Expenditure. Vol. 25, No. 8, Lippincott and Wilkins.
- Little,R.J.A. and Rubin,D.B. (2002) Statistical analysis with missing data. 2nd Edition, Wiley, NY.
- Rubin,D.B. (1986) Statistical matching using file concentration with adjusted weights and multiple imputation. J. Business & Econ. Stat. 4, 87-94.