التعامل مع مشاکل القيم المفقودة في البيانات الطولية

اٌّشاعغ Enders, C.K. (2011). Missing Not at Random Models for Latent Growth Curve Analyses. Psychological Methods,Vol. 16, No. 1, 1–16. Fitzmaurice, G. (2008). Missing data: implications for analysis. Nutrition, 24, 200-202. Gad, A.M. and Darwish, N.M. (2013). A Shared Parameter Model for Longitudinal Data with Missing Values. American Journal of 17 Applied Mathematics and Statistics, Vol. 1, No. 2, 30-35. Hair, J., Anderson, R., Tatham, R. and Black, W. (1998). Multivariate Data Analysis. Upper Saddle River, N.J.: Prentice Hall. Hedeker, D. and Gibbons R. (2006). Longitudinal Data Analysis. John Wily& Sons, Inc., Hoboken, New Jersey. Ibrahim, J. G., Chen, M. H., Lipsitz, S. R. and Herring A. H. (2005). Missing-Data Methods for Generalized Linear Models: A Comparative Review. Journal of the American Statistical Association, Vol. 100, No. 469, 332-346 Little R. J. A. (1995). Modeling the drop-out mechanism in repeatedmeasures studies. J. Am Stat Assoc; 90:1113– 1121. Little, R. J. A. and Rubin, D.B. (2002). Statistical Analysis with Missing Data (2nd ed.). New York: Wiley. Ramadan, M. (2005). Extentions of the expectationmaximization (EM) algorithm using a baysian approach. Unpublished M.Sc Dissertation, Benha University, Dept. of Statistic. 18 Rubin, D.B. (1976). Inference and missing data. Biometrika, 63,581-592. Rubin D.B. (1987). Multiple Imputation for Nonresponse in Surveys. New York, NY, Wiley. Sheir, K., Elhalwagy, S., Abo-Elghar, M. (2008) Evaluation of a synchronous twin-pulse technique for shock wave lithotripsy: a prospective randomized study of effectiveness and safety in comparison to standard single-pulse technique. B J U I N T E R N A T I O N A L | 101 , 1 4 2 0 – 1 4 2 5 Tshering, S., Okazaki, T., and Endo, S. (2013). A Method to Identify Missing Data Mechanism in Incomplete Dataset. IJCSNS International Journal of Computer Science and Network Security, 13, No.3. Wu, L. (2009). Mixed effects models for complex data. London: Chapman & Hall