Volume 19, Issue 2 (12-2020)                   JIRSS 2020, 19(2): 133-143 | Back to browse issues page

XML Persian Abstract Print

Department of Statistics, Faculty of Science, Razi University, Kermanshah, Iran. , i.almasi@razi.ac.ir
Abstract:   (104 Views)
There are several methods to make inferences about the parameters of the sampling distribution when we encounter the missing values and the censored data. In this paper, through the order statistics and the projection theorem, a novel algorithm is proposed to impute the missing values in the multivariate case. Then, the performance of this method is investigated through the simulation studies. In an attempt to validate the proposed method and compare it with some other methods a real data is used.
Full-Text [PDF 194 kb]   (6 Downloads)    
Type of Study: Original Paper | Subject: 62Jxx: Linear inference, regression
Received: 2019/06/7 | Accepted: 2020/10/30 | Published: 2020/12/11

1. Allison, P. D. (2001), Missing data (Vol. 136). Sage publications. [DOI:10.4135/9781412985079]
2. Almasi, I., Mohammadpour, A. and Mohammadi, M. (2017), Best linear unbiased interpolation of order statistics. Communications in Statistics-Simulation and Computation, 46(5), 4161-4171.
3. Asgharzadeh, A., Ahmadi, J., Mirzazadeh Ganji, Z. and Valiollahi, R. (2012), Reconstruction of the past failure times for the proportional reversed hazard rate model. Journal of Statistical Computation and Simulation, 82(3), 475-489. [DOI:10.1080/00949655.2010.542550]
4. Balakrishnan, N. and Cohen, A. C. (2014), Order statistics and inference: estimation methods. Elsevier.
5. Brockwell, P. J. and Davis, R. A. (1991), Time series: theory and methods. second ed., Springer Series in Statistics, Springer-Verlag, New York. [DOI:10.1007/978-1-4419-0320-4]
6. Dempster, A. P., Laird, N. M. and Rubin, D. B. (1977), Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical {society. Series B (methodological), 1-38. [DOI:10.1111/j.2517-6161.1977.tb01600.x]
7. Fleiss, J. L., Levin, B. and Paik, M. C. (2013)], Statistical methods for rates and proportions. John Wiley and Sons.
8. Gold, M. S. and Bentler, P. M. (2000), Treatments of missing data: A {Monte Carlo comparison of rbhdi, iterative stochastic regression imputation, and expectation-maximization. Structural Equation Modeling, 7(3), 319-355. [DOI:10.1207/S15328007SEM0703_1]
9. Greenland, S. and Finkle, W. D. (1995), A critical look at methods for handling missing covariates in epidemiologic regression analyses. American {Journal of Epidemiology, 142(12), 1255-1264. [DOI:10.1093/oxfordjournals.aje.a117592]
10. Haitovsky, Y. (1968), Missing data in regression analysis. Journal of the Royal Statistical Society. Series B (Methodological), 67-82. [DOI:10.1111/j.2517-6161.1968.tb01507.x]
11. Khatib, B., Ahmadi, J. and Razmkhah, M. (2013), Bayesian reconstruction of the missing failure times in exponential distribution. Journal of Statistical Computation and Simulation, 83(3), 501-517. [DOI:10.1080/00949655.2011.621123]
12. Kim, J. K. and Yu, C. L. (2011), A semiparametric estimation of mean functionals with nonignorable missing data. Journal of the American Statistical Association, 106(493), 157-165. [DOI:10.1198/jasa.2011.tm10104]
13. Klimczak, M. and Rychlik, T. (2005), Reconstruction of previous failure times and records. Metrika, 61(3), 277-290. [DOI:10.1007/s001840400344]
14. Little, R. J. and Rubin, D. B. (2019), Statistical analysis with missing data (Vol. 793). John Wiley and Sons. [DOI:10.1002/9781119482260]
15. McLachlan, G. and Krishnan, T. (2007), The EM algorithm and extensions (Vol. 382). John Wiley and Sons. [DOI:10.1002/9780470191613]
16. Musil, C. M., Warner, C. B., Yobas, P. K. and Jones, S. L. (2002), A comparison of imputation techniques for handling missing data. Western Journal of Nursing Research, 24(7), 815-829. [DOI:10.1177/019394502762477004]
17. Razmkhah, M., Khatib, B. and Ahmadi, J. (2010), Reconstruction of order statistics in exponential distribution. Journal of the Iranian Statistical Society, 9, 21-40.
18. Rubin, D. B. (1976), Inference and missing data. Biometrika, 63(3), 581-592. [DOI:10.1093/biomet/63.3.581]
19. Snedecor, G. and Cochran, W. G. (1967), Statistical Methods, 6th, ed. Ames: Iowa State University Press.
20. Yuan, Y. and Yin, G. (2010), Bayesian quantile regression for longitudinal studies with nonignorable missing data. Biometrics, 66(1), 105-114. [DOI:10.1111/j.1541-0420.2009.01269.x]