Multiple imputation in quantile regression

Biometrika

Volumn 99, Issue 2, 2012, Pages 423-438

  Wei, Ying ^a   Ma, Yanyuan ^b   Carroll, Raymond J ^b  

^a Och Spine at New York Presbyterian Hospitals   (United States)

^b TEXAS A AND M UNIVERSITY   (United States)

Author keywords

Missing data; Multiple imputation; Quantile regression; Regression quantile; Shrinkage estimation

Indexed keywords

EID: 84861627743     PISSN: 00063444     EISSN: 14643510     Source Type: Journal    
DOI: 10.1093/biomet/ass007     Document Type: Article

References (17)

1. AMEMIYA, T. (1985). Advanced Econometrics. Boston: Harvard University Press.
2. CARROLL, R. J., RUPPERT, D., STEFANSKI, L. A. & CRAINICEANU, C. M. (2006). Measurement Error in Nonlinear Models: A Modern Perspective, 2nd ed. London: Chapman and Hall CRC Press.
3. CHEN, Y. H., CHATTERJEE, N. & CARROLL, R. J. (2009). Shrinkage estimators for robust and efficient inference in haplotype-based case-control studies. J. Am. Statist. Assoc. 104, 220-33.
4. HALL, P. & SHEATHER, S. (1988). On the distribution of a studentized quantile. J. R. Statist Soc. B 50, 381-91.
5. HE, X. & SHAO, Q. M. (1996). A general Bahadur representation of M-estimators and its application to linear regression with nonstochastic designs. Ann. Statist. 24, 2608-30.
6. KOENKER, R. (2005). Quantile Regression. Cambridge: Cambridge University Press.
7. KOENKER, R. & BASSETT, G. J. (1978). Regression quantiles. Econometrica 46, 33-50.
8. KOENKER, R. & XIAO, Z. J. (2004). Unit root quantile autoregression inference. J. Am. Statist. Assoc. 99, 775-87.
9. LIPSITZ, S. R., FITZMAURICE, G. M.,MOLENBERGHS, G. & ZHAO, L. P. (1997). Quantile regression methods for longitudinal data with drop-outs: application to CD4 cell counts of patients infected with the human immunodeficiency virus. Appl. Statist. 46, 463-76.
10. LITTLE, R. J. A. & RUBIN, D. B. (1987). Statistical Analysis with Missing Data. New York: Wiley.
11. ROBINS, J. M., ROTNITZKY, A. & ZHAO, L. P. (1995). Analysis of semiparametric regression models for repeated outcomes in the presence of missing data. J. Am. Statist. Assoc. 90, 106-21.
12. SUBAR, A. F., THOMPSON, F. E.,KIPNIS, V.,MIDTHUNE, D.,HURWITZ, P.,MCNUTT, S.,MCINTOSH, A. & ROSENFELD, S. (2001). Comparative validation of the Block, Willett, and National Cancer Institute Food Frequency Questionnaires: the Eating at American's Table Study. Am. J. Epidemiol. 154, 1089-99.
13. TSIATIS, A. A. (2006). Semiparametric Theory and Missing Data. New York: Springer.
14. VAN DER VAART, A. W. (1998). Asymptotic Statistics. Cambridge: Cambridge University Press.
15. WEI, Y. & CARROLL, R. J. (2009). Quantile regression with measurement error. J. Am. Statist. Assoc. 104, 1129-43.
16. WELSH, A. H. (1988). Asymptotically efficient estimation of the sparsity function at a point. Statist. Prob. Lett. 6, 427-32.
17. YI, G. Y. & HE, W. (2009). Median regression models for longitudinal data with dropouts. Biometrics 65, 618-25.