Advertisement

Journal of Statistical Theory and Practice

, Volume 1, Issue 1, pp 141–146 | Cite as

Life and Work of C. R. Rao

  • Sat Gupta
Article

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson, K.M., 1991. A nonproportional hazards Weibull accelerated failure time regression model. Biometrics 47. 281–288.CrossRefGoogle Scholar
  2. Allen D.M., 1974. The relationship between variable selection and data augmentation and a method for prediction. Technometrics 16(1), 125–127.MathSciNetCrossRefGoogle Scholar
  3. Barker, P., Henderson, R., 2005. Small sample bias in the gamma frailty model for univariate survival. Lifetime Data Analysis 11, 265–284.MathSciNetCrossRefGoogle Scholar
  4. Carlin, B.P., Louis, T.A., 1996. Bayes and Empirical Bayes Methods for Data Analysis, Chapman & Hall, London.zbMATHGoogle Scholar
  5. Chen, M.H., Ibrahim, J.G., Sinha D., 2002. Bayesian inference for multivariate survival data with a cure fraction. Journal of Multivariate Analysis 80(1), 101–126.MathSciNetCrossRefGoogle Scholar
  6. Cui S., Sun Y., 2004. Checking for the gamma frailty distribution under the marginal proportional hazards frailty model. Statistica Sinica 14, 249–267.MathSciNetzbMATHGoogle Scholar
  7. D‘Agostino, R.B., Nam, B-H., 2004. Evaluation of the performance of survival analysis models: Discrimination and calibration measures. In Balakrishnan, N. and Rao, C.R.(eds.), Handbook of Statistics 23: Advances in survival analysis, Elsevier, North Holland, 1–25.Google Scholar
  8. Dawber, T.R., Meadors, G.F., Moore, F.E.J., 1951. Epidemiological approaches to heart disease: the Framingham Study. American Journal of Public Health 41, 279–286.CrossRefGoogle Scholar
  9. Dellaportas, P., Smith, A.F.M., 1993. Bayesian inference for generalized linear and proportional hazards models via Gibbs sampling. Applied Statistics 42, 443–59.MathSciNetCrossRefGoogle Scholar
  10. Fine, J.P., Kosorok, M.R., Lee, B.L., 2004. Robust inference for univariate proportional hazards frailty regression models. Annals of Statistics 32(4), 1448–1491.MathSciNetCrossRefGoogle Scholar
  11. Gilks, W.R., Richardson S., Spiegelhalter, D.J., 1996. Markov chain Monte Carlo in practice, Chapman & Hall/CRC, Boca Raton.zbMATHGoogle Scholar
  12. Govindarajulu, U.S., Sullivan, L., D‘Agostino, Sr. R.B., 2004. SAS macros for confidence interval estimation for CHD risk prediction by different survival models. Computational Data Analysis and Statistics 46(3), 571–592.MathSciNetCrossRefGoogle Scholar
  13. Heckman, J.J., Singer, B., 1984. A method for minimizing the impact of distributional assumptions in econometric models of duration data. Econometrics 52, 271–320.MathSciNetCrossRefGoogle Scholar
  14. Hougaard, P., 1984. Life table methods for heterogeneous populations: distributions describing the heterogeneity. Biometrika 71(1), 75–83.MathSciNetCrossRefGoogle Scholar
  15. Hougaard, P., 1995. Frailty models for survival data. Lifetime Data Analysis 1, 255–273.CrossRefGoogle Scholar
  16. Jones, G., 2004. Markov Chain Monte Carlo methods for inference in frailty models with doubly-censored data. Journal of data science 2, 33–47.Google Scholar
  17. Keiding, N., Andersen, P.K., Klein, J.P., 1997. The role of frailty models and accelerated failure time models in describing heterogeneity due to omitted covariates. Statistics in Medicine 16, 215–224.CrossRefGoogle Scholar
  18. Klein, J.P., Moeschberger, M.L., 1997. Survival analysis: Techniques for censored and truncated data. Springer, New York.CrossRefGoogle Scholar
  19. Lancaster, T., 1979. Econometric methods for the duration of unemployment. Econometrica 47(4), 939–956.Google Scholar
  20. Lehmann, E.L., Casella, G., 1998. Theory of point estimation. Springer-Verlag, New York.zbMATHGoogle Scholar
  21. Pickles, A., Crouchley, R., 1995. A comparison of frailty models for multivariate survival data. Statistics in Medicine 14, 1447–1461.CrossRefGoogle Scholar
  22. SAS, Version 8.2, Copyright © 1999 by SAS Institute Inc., Cary, NC, USA.Google Scholar
  23. Smith, A.F.M., Gelfand, A. E., 1992. Bayesian statistics without tears: A sampling-resampling perspective. The American Statistician 46, 84–88.MathSciNetGoogle Scholar
  24. Spiegelhalter, D.J., Thomas, A., Best, N.G., 2003. WinBugs Version 1.4 User Manual, MRC Biostatistics Unit., Cambridge, UK.Google Scholar
  25. Stone, M., 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society. Series B (Methodological) 36(2), 111–147.MathSciNetzbMATHGoogle Scholar
  26. Vaupel, J.W., Manton, K.G., Stallard, E., 1979. The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography 16(3), 439–454.CrossRefGoogle Scholar
  27. Wassell, J.T., Moeschberger, M.L., 1993. A bivariate survival model with modified gamma frailty for assessing the impact of interventions, Statistics in Medicine 12, 241–248.CrossRefGoogle Scholar
  28. Wienke A., 2003. Frailty Models. MPIDR Working Paper WP 2003-032. https://doi.org/www.demogr.mpg.de. Google Scholar
  29. Wolf, P.A., Abbott, R.D., Kannel, W.B., 1987. Atrial fibrillation: a major contributor to stroke in the elderly. The Framingham Study. Archives of Internal Medicine 147(9), 1561–4.Google Scholar
  30. Wolf, P.A., Dawber T.R., Thomas, H.E.J., and Kannel, W.B. (1978). Epidemiologic assessment of chronic atrial fibrillation and risk of stroke: the Framingham Study. Neurology 28, 973–977.CrossRefGoogle Scholar

Copyright information

© Grace Scientific Publishing 2007

Authors and Affiliations

  • Sat Gupta
    • 1
  1. 1.Department of Mathematics and StatisticsUniversity of North Carolina at GreensboroGreensboroUSA

Personalised recommendations