Convergence Properties of a Sequential Regression Multiple Imputation Algorithm

Journal of the American Statistical Association

Volumn 110, Issue 511, 2015, Pages 1112-1124

  Zhu, Jian ^a   Raghunathan, Trivellore E ^a  

^a UNIVERSITY OF MICHIGAN   (United States)

Author keywords

Bayesian analysis; Chained equations; Compatible conditionals; Conditional specifications; Exponential family; Gibbs sampling; Missing data

Indexed keywords

EID: 84946962849     PISSN: 01621459     EISSN: 1537274X     Source Type: Journal    
DOI: 10.1080/01621459.2014.948117     Document Type: Article

References (15)

1. T.W.Anderson, (1957), Maximum Likelihood Estimates for the Multivariate Normal Distribution When Some Observations are Missing, Journal of the American Statistical Association, 52, 200–203.
2. B.C.Arnold,, E.Castillo,, J.M.Sarabia, (2001), Conditionally Specified Distributions: An Introduction (with discussion), Statistical Science, 16, 249–274.
3. B.C.Arnold,, S.J.Press, (1989), Compatible Conditional Distributions, Journal of the American Statistical Association, 84, 152–156.
4. B.C.Arnold,, D.J.Strauss, (1991), Bivariate Distributions With Conditionals in Prescribed Exponential Families, Journal of the Royal Statistical Society, 53, 365–375.
5. J.Drechsler,, S.Rässler, (2008), Does Convergence Really Matter?” Recent Advances in Linear Models and Related Areas, eds. Shalabh and Heumann, Heidelberg: Physica-Verlag,. 341–355.
6. A.Gelman,, T.E.Raghunathan, (2001), Discussion of “Conditionally Specified Distributions,” by Arnold et al. Statistical Science, 16, 249–274.
7. A.Gelman,, T.P.Speed, (1993), Characterizing a Joint Probability Distribution by Conditionals, Journal of the Royal Statistical Society,55, 185–188.
8. A.B.Kennickell, (1991), Imputation of the 1989 Survey of Consumer Finances: Stochastic Relaxation and Multiple Imputation, Proceedings of the Second Survey Research Methods America Statistics Association,. 1–10.
9. F.Li,, M.Baccini,, F.Mealli,, E.R.Zell,, C.E.Frangakis,, D.B.Rubin, (2014), Multiple Imputation by Ordered Monotone Blocks With Application to the Anthrax Vaccine Research Program, Journal of Computational and Graphical Statistics, 23, 877–892.
10. F.Li,, Y.Yu,, D.B.Rubin, (2012), Imputing Missing Data by Fully Conditional Models: Some Cautionary Examplesand Guidelines, technical report, Department of Statistical Science, Duke University. Discussion Paper Number 11–24. Available at https://stat.duke.edu/~fl35/publications.html.
11. T.E.Raghunathan,, J.M.Lepkowski,, J.VanHoewyk,, P.Solenberger, (2001), A Multivariate Technique for Multiply Imputing Missing Values Using a Sequence of Regression Models, Survey Methodology, 27, 85–95.
12. P.Royston, (2005), Multiple Imputation of Missing Values: Update, Stata Journal, 5, 188–201.
13. D.B.Rubin, (1976), Inference and Missing Data, Biometrika, 63, 581–590.
14. S.van Buuren,, J.P.L.Brand,, C.G.M.Groothuis-Oudshoorn,, D.B.Rubin, (2006), Fully Conditional Specification in Multivariate Imputation, Journal of Statistical Computation and Simulation, 76, 1049–1064.
15. S.van Buuren,, C.Oudshoorn, (1999), Flexible Multivariate Imputation by Mice, Technical Report No. TNO-rapport PG 99.054, TNO Prevention and Health, Leiden.