Adaptive penalized M-estimation with current status data

Annals of the Institute of Statistical Mathematics

Volumn 58, Issue 3, 2006, Pages 511-526

  Ma, Shuangge ^a   Kosorok, Michael R ^b  

^a YALE UNIVERSITY   (United States)

^b University of North Carolina at Chapel Hill   (United States)

Author keywords

Adaptive semiparametric estimation; Current status data; Penalized M estimator

Indexed keywords

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD ESTIMATION;

ADAPTIVE SEMIPARAMETRIC ESTIMATION; COX MODEL; CURRENT STATUS DATA; PENALIZED M-ESTIMATOR;

DATA REDUCTION;

EID: 33748539415     PISSN: 00203157     EISSN: 15729052     Source Type: Journal    
DOI: 10.1007/s10463-005-0026-4     Document Type: Article

References (30)

1. Barbe, P., Bertail, P. (1995). The weighted bootstrap. New York: Springer
2. Cai, T., Betensky, R.A. (2003). Hazard regression for interval-censored data with penalized spline. Biometrics 59, 570-579.
3. Cox, D.R. (1972) Regression models and lifetables (with discussion). Journal of the Royal Statistical Society, Series B 34, 187-22.
4. Diamond, I. D., McDonald, J. W., Shah, I. H. (1986). Proportional hazards models for current status data: application to the study of differentials in age at weaning in Pakistan. Demography 23, 607-620.
5. Gart, J.J., Krewski, D., Lee, P.N., Tarone, R.E., Wahrendorf, J. (1986). Statistical methods in cancer research, volume III, the design and analysis of longterm Animal experiments. IARC Scientific Publications NO. 79. Lyon: International Agency for Research on Cancer.
6. Groeneboom, P., Wellner, J. A. (1992). Information bounds and nonparametric maximum likelihood estimation. Basel: Birkhauser
7. Grummer-Strawn, L. M. (1993). Regression analysis of current status status data: An application to breast feeding. Journal of the American Statistical Association 88, 758-765.
8. Gu, C. (2002). Smoothing spline ANOVA models. Newyork: Springer
9. Gui, J., Li, K. (2004). Penalized cox regression analysis in the high-dimensional and low-sample size settings, with applications to microarray gene expression data. Center for Bioinformatics and Molecular Biostatistics.
10. Hall, P., Huang, L.S. (2001). Nonparametric kernel regression subject to monotonicity constraints. Annals of Statistics 29, 624-64.
11. Huang, J. (1996). Efficient estimation for the proportional hazard model with interval censoring. The Annals of Statistics 24, 540-568.
12. Jewell, N. P., Shiboski, S.C. (1990). Statistical analysis of HIV infectivity based on partner studies. Biometrics 46, 1133-1150.
13. Jewell, N.P., Van der Laan, M.J. (2002). Current status data: review, recent developments and open problems. Technical Report: Department of Biostatistics, UC-Berkeley.
14. Keiding, N., Begtrup, K., Scheike, T.H., Hasibeder, G. (1996). Estimation from current status data in continuous time. Lifetime Data Analysis 2, 119-129.
15. Ma, S. (2004). Penalized M-estimation for partly linear transformation models with current status data. PhD Thesis, Department of Statistics, UW-Madison.
16. Ma, S., Kosorok, M.R. (2004a). Robust semiparametric M-estimation and the weighted bootstrap. Journal of Multivariate Analysis (in press).
17. Ma, S., Kosorok, M.R. (2004b). Penalized log-likelihood estimation for partly linear transformation models with current status data. Annals of Statistics (in press).
18. Murphy, S.A., Van der Vaart, A.W. (2000). On profile likelihood. Journal of the American Statistical Association 95, 449-485.
19. Rossini, A.J., Tsiatis, A.A. (1996). A semiparametric proportional odds regression model for the analysis of current status data. Journal of the American Statistical Association 93, 713-721.
20. Shen, X. (2000). Linear regression with current status data. Journal of the American Statistical Association 95, 842-852.
21. Shiboski, S.C. (1998). Generalized additive models for current status data. Lifetime Data Analysis 4, 29-50.
22. Smyth, P. (2001). Model selection of probabilistic clustering using cross-validated likelihood. Statistics and Computing 10, 63-72.
23. Van de Geer, S. (2000). Empirical processes in M-estimation. Cambridge Series in Statistical and Probabilistic Mathematics.
24. Van de Geer, S. (2001). Least square estimation with complexity penalties. Mathematical Methods of statistics 10, 355-374.
25. Van der Laan, M.J. (1995). Locally efficient estimation with current status data and high dimensional covariates. Group in Biostatistics Technical Report 55. University of California, Berkeley.
26. Van der Laan, M.J., Bickel, P., Jewell, N. P. (1994). Singly and doubly censored current status data: Estimation, asymptotics and regression. Group in Biostatistics Technical Report 50, University of California, Berkeley.
27. Van der Vaart, A.W., Wellner, J.A. (1996). Weak Convergence and Empirical Processes: With Applications to Statistics. New York: Springer
28. Wahba, G. (1990) Spline models for observational data. SIAM. CBMS-NSF Regional Conference Series in Applied Mathematics.
29. Wahba, G., Wendelberger, J. (1980). Some New Mathematical Methods for Variational Objective Analysis Using Splines and Cross Validation Monthly Weather Review 108, 1122-1143.
30. Xiang, D., Wahba, G. (1997) Approximate smoothing spline methods for large data set in the binary case. Technical Report, Department of Statistics, UW-Madison.