Variable selection in the presence of missing data: Resampling and imputation

Biostatistics

Volumn 16, Issue 3, 2015, Pages 596-610

  Long, Qi ^a   Johnson, Brent A ^b  

^a EMORY UNIVERSITY   (United States)

^b UNIVERSITY OF ROCHESTER   (United States)

Author keywords

Bootstrap imputation; Missing data; Resampling; Stability selection; Variable selection.

Indexed keywords

TUMOR MARKER;

BIOSTATISTICS; COMPUTER SIMULATION; FEMALE; GENETICS; HUMAN; MALE; MONTE CARLO METHOD; PROSTATE TUMOR; REGISTER; STATISTICAL ANALYSIS; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA; STROKE;

BIOMARKERS, TUMOR; BIOSTATISTICS; COMPUTER SIMULATION; DATA INTERPRETATION, STATISTICAL; FEMALE; HUMANS; LINEAR MODELS; MALE; MODELS, STATISTICAL; MONTE CARLO METHOD; PROSTATIC NEOPLASMS; REGISTRIES; STROKE;

EID: 84936746626     PISSN: 14654644     EISSN: 14684357     Source Type: Journal    
DOI: 10.1093/biostatistics/kxv003     Document Type: Article

References (24)

1. AKAIKE, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control 19(6), 716-723
2. BACH, F. (2008). Bolasso: model consistent lasso estimation through the bootstrap. In Proc. 25th Int. Conf. Machine Learning, pp. 33-40. New York: Association for Computing Machinery
3. BACH, F. (2009). Model-consistent sparse estimation through the bootstrap. Technical Report, arXiv:0901.3202
4. BUUREN, S. AND GROOTHUIS-OUDSHOORN, K. (2011). Mice: multivariate imputation by chained equations in r. Journal of Statistical Software 45(3), 1-67
5. CHATTERJEE, A. AND LAHIRI, S. N. (2011). Bootstrapping lasso estimators. Journal of the American Statistical Association 106, 608-625
6. CHEN, Q. AND WANG, S. (2013). Variable selection for multiply-imputed data with application to dioxin exposure study. Statistics in medicine 32(21), 3646-3659
7. EFRON, B. (1994). Missing data, imputation, and the bootstrap. Journal of the American Statistical Association 89, 463-475
8. EFRON, B., HASTIE, T., JOHNSTONE, I. AND TIBSHIRANI, R. (2004). Least angle regression. The Annals of statistics 32(2), 407-499
9. GARCIA, R. I., IBRAHIM, J. G. AND ZHU, H. (2009). Variable selection in the cox regression model with covariates missing at random. Biometrics 66(1), 97-104
10. GARCIA, R. I., IBRAHIM, J. G. AND ZHU, H. (2010). Variable selection for regression models with missing data. Statistica Sinica 20(1), 149
11. GEORGE, E. I. AND MCCULLOCH, R. E. (1997). Approaches for bayesian variable selection. Statistica Sinica 7, 339-374
12. HEYMANS, M.W., VAN BUUREN, S., KNOL, D. L., VAN MECHELEN, W. AND DE VET, H. C.W. (2007). Variable selection under multiple imputation using the bootstrap in a prognostic study. BMC Medical Research Methodology 7(1), 33
13. JOHNSON, B. A., LIN, D. Y. AND ZENG, D. (2008). Penalized estimating functions and variable selection in semiparametric regression models. Journal of the American Statistical Association 103, 672-680
14. LACHENBRUCH, P. A. (2011). Variable selection when missing values are present: a case study. Statistical Methods in Medical Research 20(4), 429-444
15. LITTLE, R. J. A. AND RUBIN, D. B. (2002) Statistical Analysis with Missing Data. New York: John Wiley
16. MEINSHAUSEN, N. AND BUHLMANN, P. (2010). Stability selection. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 72(4), 417-473
17. RAGHUNATHAN, T. E., LEPKOWSKI, J. M., VAN HOEWYK, J. AND SOLENBERGER, P. (2001). A multivariate technique for multiply imputing missing values using a sequence of regression models. Survey Methodology 27(1), 85-96
18. SU, Y. S., GELMAN, A., HILL, J. AND YAJIMA, M. (2011). Multiple imputation with diagnostics (mi) in r: opening windows into the black box. Journal of Statistical Software 45(2), 1-31
19. TIBSHIRANI, R. J. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B 58, 267-288
20. TSIATIS, A. (2006) Semiparametric theory and missing data. Berlin: Springer
21. WOLFSON, J. (2011). Eeboost: a general method for prediction and variable selection based on estimating equations. Journal of the American Statistical Association 106(493), 296-305
22. WOOD, A. M., WHITE, I. R. AND ROYSTON, P. (2008). How should variable selection be performed with multiply imputed data? Statistics in Medicine 27(17), 3227-3246
23. YANG, X., BELIN, T. R. AND BOSCARDIN, W. J. (2005). Imputation and variable selection in linear regression models with missing covariates. Biometrics 61(2), 498-506
24. ZHAO, Y. AND LONG, Q. (2013). Multiple imputation in the presence of high-dimensional data. Statistical Methods in Medical Research, in press