參考文獻 |
[1] Anderson, P. K. and Gill, R. D. (1982). Cox’s regression model forcounting processes, a large sample study. Annals of Statistics 10.1100-1120.
[2] Burden, Richard L. and Faires, J. Douglas (2000). Numerical Analysis (7th ed.). Brooks/Cole
[3] Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society 34(B). 187-220.
[4] Cox, D. R. (1975). Partial likelihood. Biometrika 62. 269-276.
[5] Efron, B. (1994). Missing data, imputation and bootstrap (with Discussion). J. Am. Statist. Assoc. 89. 463-479.
[6] Hsieh, F., Tseng, Y. K., and Wang, J. L. (2006). Joint modelingof survival time and longitudinal data: likelihood approach revisit.Biometrics, 62.1037-1043.
[7] Klein, J. P. and M. L. Moeschberger (1997). Survival Analysis: Techniques for Censored and Truncated Data. Springer.
[8] Lawless, J. F. (1982). Statistical Models and Methods for Lifetime Data. John Wiley & Sons, Inc., New York.
[9] Louis, T. A. (1982). Finding the observed Fisher information when using the EM algorithm. Journal of the Royal Statistical Society, Series B 44. 226-233.
[10] Lin, D. Y. and Ying, Z. (1993). Cox regression with incomplete covariate measurements. Journal of the American Statistical Association 88(424). 1341-1349.
[11] Orchard, T. and Woodbury, M. A. (1972). A missing information principle: Theory and applications. In Proceedings of the 6th Berkeley Symposium on Mathematical Statistics and Probability, Volume 1,697-715. Berkeley: University of California Press.
[12] Prentice, R. L. (1982). Covariate measurement errors and parameter estimation in failure time regression model. Biometrika 69. 331-342
[13] Press, W. H., Teutolsky, S. A., Vetterling, W. T., and Flannery,B.P. (1992). Numerical recipes in FORTRAN: the art of scientific computing. New York, NY, USA: Cambridge University Press, 2nd ed.
[14] Paik, M. C., and Tsai, W. Y. (1997). On using the Cox proportional hazards model with missing covariates. Biometrika 84(3). 579-593.
[15] Schafer, D.W.(1987). Covariate measurement errors in generalized linear models. Biometrika 74. 385-391.
[16] Tierney, L. and Kadane, J. B. (1986). Accurate approximation for posterior moments and marginal densities. Journal of the American Statistical Association 81. 82-86.
[17] Tseng, Y. K., Hsieh F. and Wang J.L. (2005). Joint modeling of accelerated failure time and longitudinal data., Biometrika 92. 587-603.
[18] Tseng, Y. K. and Hsieh, Y. H.. A Joint model approach for evaluating the efficacy of HAART treatment for AIDS patients in Taiwan. Manuscript.
[19] Wulfsohn, M. S. and Tsiatis, A. A. (1997). A joint model for survival and longitudinal data measured with error. Biometrics 53. 330-339.
[20] Wang, Y. and Taylor, J. M. G. (2001). Jointly modeling longitudinal and event time data with application to acquired immunodeficiency syndrome. Journal of the American Statistical Association 96. 895-905.
[21] Zhou, H. and Pepe, M. S. (1995). Auxiliary covariate data in failure time regression. Biometrika 82. 139-149.
[22] Zeng D. and Cai J. (2005). Asymptotic results for maximum likelihood estimators in joint analysis of repeated measurements and survival time. The Annals of Statistics 33(5). 2132-63.
|