Increase Efficiency of Missing Data Imputation by Regime Switching
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Abstract
The aim of this paper is to propose regime switching approach to missing data imputation for increasing efficiency by regime switching mean and regime switching regression. By using simulation data, the comparisons were made between mean imputation with regime switching mean and between regression imputation with regime switching regression. Under MSE regime switching mean outperformed the mean imputation and regime switching regression outperformed the regression imputation method.
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Applied Science Research Articles
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References
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[3] A. M. Wood, I. R. White, and S. G. Thompson, “Are missing outcome data adequately handled: A review of published randomized controlled trials in major medical journals,” Clinical Trial. vol. 1, pp 368–376, 2004.
[4] N. Tsikriktsis, “A review of techniques for treating missing data in OM survey research,” Journal of Operations Management, vol. 24, pp. 53–62, 2005.
[5] P. Ogoke Uchenna and E. C. Nduka, “Methods of analysing missing values in a regression model,” Indian Journal of Science and Technology, vol. 5, no. 2, pp. 2013–2016, 2012.
[6] M. R. Raymond, “Missing data in evaluation research,” Evaluation & the Health Professions, vol. 9, no. 4, pp. 395–420, 1986.
[7] J. W. France, “Some simple procedures for handling missing data in multivariate analysis,” Psychometrika, vol. 41, pp. 409–415, 1976.
[8] L. L. Brockmeier, J. D. Kromrey, and C. V. Hines, “Systematically missing data and multiple regression analysis : An empirical comparison of election and imputation techniques,” Multiple Linear Regression Viewpoints, vol. 25, pp. 20–39, 1998.
[9] N. H. Timm, “The estimation of variancecovariance and correlation matrices from incomplete data,” Psychometrika, vol. 35, no. 4, pp. 417–437, 1970.
[10] E. M. L. Beale and R.J.A. Little, “Missing values in multivariate analysis,” Journal of the Royal Statistical Society, vol. 37, pp. 129–145, 1975.
[11] T. C. Gleason and R. Staelin, “A proposal for handling missing data,” Psvchometrika, vol. 40, pp. 229–252, 1975.
[12] L. S. Chan, J. A. Gilman, and O. J. Dunn, “Alternative approaches to missing values in discriminant analysis,” Journal of the American Statistical Association, vol. 71, pp. 842–844, 1976.
[13] R. J. A. Little, “Inference about means from incomplete multivariate data,” Biometrika, vol. 63, pp. 593–604, 1976.
[14] M. R. Raymond and D. M. Roberts, “A comparison of methods for treating incomplete data in selection research,” Educational and Psychological Measurement, vol. 47, pp. 13–26, 1987.
[15] J. D. Hamilton, “A new approach to the economic analysis of nonstationary time series and the business cycle,” Econometrica, vol. 57, pp. 357–384, 1989.