1. Satten GA, Carroll RJ. Conditional and Unconditional Categorical Regression Models with Missing Covariates. Biometrics 2000;56:384-388. [
10.1111/j.0006-341X.2000.00384.x]
2. Fleiss J.L, Levin B, Paik M.C. Statistical Methods for Rates and Proportions. 3rd Edition. New York:John Wiley & Sons 2002. ISBN 0-471-52629-0.
3. Enders CK. Applied Missing Data Analysis. New York and London: Guilford Press 2010. ISBN 978-1-60623-639-0.
4. Hosmer DW, Lemeshow S. Applied Logistic Regression. 2nd Edition. New York: John Wiley & Sons; 2000. ISBN: 0-471-72214-6. [
10.1002/0471722146]
5. Chen HY, Little R. A. test of missing completely at random for generalised estimating equations with missing data. Biometrika 1999;86:1-13. [
10.1093/biomet/86.1.1]
6. Diggle P.J. Testing for random dropouts in repeated measurement data. Biometrics 1989;45:1255-1258. [
10.2307/2531777]
7. Dixon W.J. BMDP statistical software. Los Angeles: University of California Press 1988.
8. Kim K. H, Bentler P. M. Tests of homogeneity of means and covariance matrices for multivariate incomplete data. Psychometrika 2002:67:609-624. [
10.1007/BF02295134]
9. Little R. J. A. A test of missing completely at random for multivariate data with missing values. Journal of the American Statistical Association 1988;83:1198-1202. [
10.1080/01621459.1988.10478722]
10. Muthén B, Kaplan D, Hollis M. On structural equation modeling with data that are not missing completely at random. Psychometrika 1987;52:431-462. doi: [
10.1007/BF02294365]
11. Park T, Lee S-Y. A test of missing completely at random for longitudinal data with missing observations.Statistics in Medicine 1997;16:1859-1871.
https://doi.org/10.1002/(SICI)1097-0258(19970830)16:16<1859::AID-SIM593>3.0.CO;2-3 [
DOI:10.1002/(SICI)1097-0258(19970830)16:163.0.CO;2-3]
12. Thoemmes F, Enders C. K. A structural equation model for testing whether data are missing completely at random. Paper presented at the annual meeting of the American Educational Research Association, Chicago, IL 2007 April.
13. SPSS Missing Value Analysis™ 17. SPSS Inc 2007. Printed in the United States of America. http://www.spss.com
14. Longford NT. Missing data and small area estimation. Springer 2005 ISBN-13: 978-185233-760-5.
15. Karimlou M.Jandaghi G.R. Mohammad K.Wolfe R.Azam K. A Comparison of Parameter Estimates in Standard Logistic Regressin Using WinBUGS MCMC and MLE Methods in R for Different Sample Size.Far East J.Theo.stat 2006.
16. Ibrahim J G, Chen MH, Lipsitz SR. Bayesian methods for generalized linear models with covariates missing at random. Canadian Journal of Statistics 2008 DEC 10;2307/3315865.
17. Marwala T. Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques. South Africa: University of Witwatersrand IGI Global; 2009 ISBN 978-1-60566-336-4. [
DOI:10.4018/978-1-60566-336-4]