Statistical inference based on non-smooth estimating functions

Biometrika

Volumn 91, Issue 4, 2004, Pages 943-954

  Tian, L ^a   Liu, J ^b   Zhao, Y ^c   Wei, L J ^c  

^a NORTHWESTERN UNIVERSITY   (United States)

^b HARVARD UNIVERSITY   (United States)

^c HARVARD SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

Importance sampling; L1 norm; Linear regression for censored data; Resampling

Indexed keywords

EID: 24144471266     PISSN: 00063444     EISSN: None     Source Type: Journal    
DOI: 10.1093/biomet/91.4.943     Document Type: Article

References (25)

1. BANG, H. & TSIATIS, A. A. (2002). Median regression with censored cost data. Biometrics 58, 643-9.
2. BARRODALE, I. & ROBERTS, F. D. K. (1977). Algorithms for restricted least absolute value estimation. Commun. Statist. B 6, 353-64.
3. BUCKLEY, J. & JAMES, I. (1979). Linear regression with censored data. Biometrika 66, 429-36.
4. EFRON, B. & TIBSHIRANI, R. (1993). An Introduction to the Bootstrap. London: Chapman and Hall.
5. FLEMING, T. R. & HARRINGTON, D. (1991). Counting Processes and Survival Analysis. New York: John Wiley and Sons.
6. GOLDWASSER, M. I., TIAN, L. & WEI, L. J. (2004). Statistical inference for infinite-dimensional parameters via asymptotically pivotal estimating functions. Biometrika 91, 81-94.
7. HU, F. & KALBFLEISCH, J. D. (2000). The estimating function bootstrap. Can. J. Statist. 28, 449-81.
8. JIN, Z., LIN, D. Y., WEI, L. J. & YING, Z. (2003). Rank-based inference for the accelerated failure time model. Biometrika 90, 341-53.
9. KALBFLEISCH, J. D. & PRENTICE, R. L. (2002). The Statistical Analysis of Failure Time Data. New York: John Wiley and Sons.
10. KOENKER, R. & BASSETT, G. J. (1978). Regression quantiles. Econometrica 46, 33-50.
11. KOENKER, R. & D'OREY, V. (1987). Computing regression quantiles. Appl. Statist. 36, 383-93.
12. KOSOROK, M. R. & LIN, C. (1999). The versatility of function-indexed weighted log-rank statistics. J. Am. Statist. Assoc. 94, 320-32.
13. LIU, J. (2001). Monte Carlo Strategies in Scientific Computing. New York: Springer.
14. NETER, J., WASSERMAN, W. & KUTNER, M. H. (1985). Applied Linear Statistical Models: Regression, Analysis of Variance, and Experimental Designs. Homewood, IL.: Richard D. Irwin, Inc.
15. OH, M. S. & BERGER, J. O. (1992). Adaptive importance sampling in Monte Carlo integration. J. Am. Statist. Assoc. 41, 143-68.
16. PARZEN, M. I., WEI, L. J. & YING, Z. (1994). A resampling method based on pivotal estimating functions. Biometrika 81, 341-50.
17. PRENTICE, R. L. (1978). Linear rank tests with censored data. Biometrika 65, 167-80.
18. RITOV, Y. (1990). Estimation in a linear regression model with censored data. Ann. Statist. 18, 303-28.
19. ROBINS, J. M. & ROTNITZKY, A. (1992). Recovery of information and adjustment for dependent censoring using surrogate markers. In Aids Epidemiology, Ed. N. P. Jewell, K. Dietz and V. T. Farewell, pp. 297-331. Boston: Birkhauser.
20. ROBINS, J. M., ROTNITZKY, A. & ZHAO, L. P. (1994). Estimation of regression coefficients when some regressors are not always observed. J. Am. Statist. Assoc. 89, 846-66.
21. RUBIN, D. (1987). A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: the SIR algorithm. J. Am. Statist. Assoc. 82, 543-6.
22. TSIATIS, A. A. (1990). Estimating regression parameters using linear rank tests for censored data. Ann. Statist. 18, 354-72.
23. VAN DER VAART, A. W. (1988). Asymptotic Statistics. New York: Cambridge University Press.
24. WEI, L. J., YING, Z. & LIN, D. Y. (1990). Linear regression analysis of censored survival data based on rank tests. Biometrika 77, 845-51.
25. YING, Z. (1993). A large sample study of rank estimation for censored regression data. Ann. Statist. 21, 76-99.