Impact of Bootstrap on the Estimating Functions

Statistical Science

Volumn 18, Issue 2, 2003, Pages 185-190

  Lele, Subhash R ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

Model robust confidence intervals; Multiple roots; Stochastic processes; Wu's wild bootstrap

Indexed keywords

EID: 0347532851     PISSN: 08834237     EISSN: None     Source Type: Journal    
DOI: 10.1214/ss/1063994973     Document Type: Review

References (27)

1. BOYCE, M. S., LELE, S. R. and JOHNS, B. W. (2003). Whooping crane recruitment enhanced by egg removal. Unpublished manuscript.
2. EFRON, B. and TIBSHIRANI, R. J. (1993). An Introduction to Bootstrap. Chapman and Hall, New York.
3. GODAMBE, V. P. (1960). An optimum property of regular maximum likelihood estimation. Ann. Math. Statist. 31 1208-1212.
4. GODAMBE, V. P. (1985). The foundations of finite sample estimation in stochastic processes. Biometrika 72 419-428.
5. GODAMBE, V. P. and HEYDE, C. C. (1987). Quasi-likelihood and optimal estimation. Internat. Statist. Rev. 55 231-244.
6. GODAMBE, V. P. and KALE, B. K. (1991). Estimating functions: An overview. In Estimating Functions (V. P. Godambe, ed.) 3-20. Oxford Univ. Press.
7. GODAMBE, V. P. and THOMPSON, M. E. (1984). Robust estimation through estimating equations. Biometrika 71 115-125.
8. GODAMBE, V. P. and THOMPSON, M. E. (1999). A new look at confidence intervals in survey sampling, Survey Methodology 25 161-173.
9. HE, X. and HU, F. (2002). Markov chain marginal bootstrap. J. Amer. Statist. Assoc. 97 783-795.
10. HEYDE, C. C. (1997). Quasi-likelihood and Its Application. Springer, New York.
11. HU, F. and KALBFLEISCH, J. D. (2000). The estimating function bootstrap. Canad. J. Statist. 28 449-499.
12. HU, F. and ZIDEK, J. V. (1995). A bootstrap based on the estimating equations of the linear model. Biometrika 82 263-275.
13. KÜNSCH, H. R. (1989). The jackknife and the bootstrap for general stationary observations. Ann. Statist. 17 1217-1241.
14. LELE, S. R. (1991a). Jackknifing linear estimating equations: Asymptotic theory and applications in stochastic processes. J. Roy. Statist. Soc. Ser. B 53 253-267.
15. LELE, S. R. (1991b). Resampling using estimating equations. In Estimating Functions (V. P. Godambe, ed.) 295-304. Oxford Univ. Press.
16. LI, B. (1996). A minimax approach to consistency and efficiency for estimating equations. Ann. Statist. 24 1283-1297.
17. LI, B. and McCULLAGH, P. (1994). Potential functions and conservative estimating functions. Ann. Statist. 22 340-356.
18. LIPSITZ, S. R., DEAR, K. B. G. and ZHAO, L. (1994). Jackknife estimators of variance for parameter estimates from estimating equations with applications to clustered survival data. Biometrics 50 842-846.
19. LIU, R. Y. (1988). Bootstrap procedures under some non-iid models. Ann. Statist. 16 1696-1708.
20. LIU, R. Y. and SINGH, K. (1992). Moving blocks jackknife and bootstrap capture weak dependence. In Exploring the Limits of Bootstrap (R. LePage and L. Billard, eds.) 225-248. Wiley, New York.
21. MARKATOU, M., BASU, A. and LINDSAY, B. G. (1998). Weighted likelihood equations with bootstrap root search. J. Amer. Statist. Assoc. 93 740-750.
22. PRASAD, N. G. N. (2002). Personal communication.
23. RAJARSHI, M. B. and GODAMBE, V. P. (2001). Confidence intervals based on estimating functions from stationary observations. Working Paper 2001-18, Dept. Statistics and Actuarial Science, Univ. Waterloo, Ontario, Canada.
24. ROYALL, R. M. (1986). Model robust confidence intervals using maximum likelihood estimators. Internat. Statist. Rev. 54 221-226.
25. SMALL, C. G., WANG, J. and YANG, Z. (2000). Eliminating multiple root problems in estimation (with discussion). Statist. Sci. 15 313-341.
26. WALD, A. (1949). Note on the consistency of the maximum likelihood estimate. Ann. Math. Statist. 20 595-601.
27. WU, C.-F. J. (1986). Jackknife, bootstrap and other resampling methods in regression analysis (with discussion). Ann. Statist. 14 1261-1350.