Advertisement

Statistics and Computing

, Volume 3, Issue 4, pp 205–208 | Cite as

Statistics research for the next ten years

  • Richard L. Smith
Papers
  • 68 Downloads

Keywords

Mathematical Modeling Data Structure Information Theory Industrial Mathematic Statistics Research 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barnett, V. and Turkman, K. F. (eds.), (1993) Statistics for the Environment. Wiley, Chichester.Google Scholar
  2. Benedicks, M. and Carleson, L. (1991) The dynamics of the Hénon map. Annals of Mathematics 133, 73–169.Google Scholar
  3. Brown, J. J. (1993) A Finite Sampling Plan, Central Limit Theorem, and Bootstrap Algorithm for a Homogeneous and Isotropic Random Field on the Three-Dimensional Sphere. Ph.D. Thesis, Department of Statistics, University of North Carolina, Chapel Hill.Google Scholar
  4. Carlstein, E. (1992) Resampling techniques for stationary time series: Some recent developments. In New Directions in Time Series Analysis, Part I, (D. Brillinger, E. Parzen, M. Rosenblatt eds.), pp. 75–85. IMA Volumes in Mathematics and its Applications, Springer-Verlag, New York.Google Scholar
  5. Christakos, G. (1992) Random Field Models in Earth Sciences. Academic Press, San Diego, CA.Google Scholar
  6. Cressie, N. (1991) Statistics for Spatial Data. Wiley, New York.Google Scholar
  7. DiCiccio, T. J. and Romano, J. P. (1988) A review of bootstrap confidence intervals. Journal of the Royal Statistical Society B, 50, 338–354.Google Scholar
  8. Gelfand, A. E. and Smith, A. F. M. (1990) Sampling-based approaches to calculating marginal densities. Journal of the American Statistical Association 85, 398–409.Google Scholar
  9. Geman, S. and Geman, D. (1984) Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 721.Google Scholar
  10. Griffeath, D. (1992) Comment: Randomness in complex systems. Statistical Science, 7, 104–108.Google Scholar
  11. Hall, P. (1992) The Bootstrap and Edgeworth Expansion. Springer-Verlag, New York.Google Scholar
  12. Handcock, M. S. and Stein, M. L. (1993) A Bayesian analysis of kriging. Technometrics, to appear.Google Scholar
  13. Handcock, M. S. and Wallis, J. R. (1993) An approach to statistical spatial-temporal modeling of meteorological fields. Journal of the American Statistical Association.Google Scholar
  14. Hastings, W. K. (1970) Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97–109.Google Scholar
  15. Hinkley, D. Bootstrap methods. Journal of the Royal Statistical Society B, 50, 321–337.Google Scholar
  16. Künsch, H. (1989) The jackknife and the bootstrap for general stationary observations. Annals of Statistics 17, 1217–1241.Google Scholar
  17. Mardia, K. V. and Marshall, R. J. (1984) Maximum likelihood estimation of models for residual covariance in spatial regression. Biometrika 71, 135–146.Google Scholar
  18. McCaffrey, D., Ellner, S., Gallant, A. R. and Nychka, D. (1992) Estimating the Lyapunov exponent of a chaotic system with nonparametric regression. Journal of the American Statistical Association, 87, 682–695.Google Scholar
  19. Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller A. H. and Teller, E. (1953) Equation of state calculations by fast computing machines. Journal of Chemical Physics, 21, 1087–1092.Google Scholar
  20. Mykland, P., Tierney, L. and Bin Yu, (1992) Regeneration in Markov chain samplers. Preprint, University of Minnesota.Google Scholar
  21. Nummelin, E. (1984) General Irreducible Markov Chains and Non-negative Operators. Cambridge University Press.Google Scholar
  22. Oehlert, G. (1993) Regional trends in sulfate wet deposition. Journal of the American Statistical Association, 88, 390–399.Google Scholar
  23. Possolo, A. (1991) Subsampling a random field. In IMS Lecture Notes, Vol. 20: Spatial Statistics and Imaging.Google Scholar
  24. Romano, J. P. (1988) A bootstrap revival of some nonparametric distance tests. Journal of the American Statistical Association, 83, 698–708.Google Scholar
  25. Ruelle, D. (1990) Deterministic chaos: the science and the fiction. Proceedings of the Royal Society of London A, 427, 241–248.Google Scholar
  26. Schmeiser, B. (1992) Comment. Statistical Science, 7, 498–499.Google Scholar
  27. Smith, R. L. (1992) Estimating dimension in noisy chaotic time series. Journal of the Royal Statistical Society B, 54, 329–352.Google Scholar
  28. Tierney, L. (1993) Markov chains for exploring posterior distributions. Annals of Statistics.Google Scholar
  29. Tong, H. (1990) Non-linear Time Series: A Dynamical Systems Approach. Oxford University Press, Oxford.Google Scholar
  30. Walden, A. and Guttorp, P. (eds.) (1992) Statistics in the Environmental and Earth Sciences. Edward Arnold, London.Google Scholar
  31. Waldrop, M. M. (1992) Complexity: The Emerging Science at the Edge of Order and Chaos. Simon & Schuster, New York.Google Scholar
  32. Wellner, J. A. (1992) Empirical processes in action: a review. International Statistical Review, 60, 247–269.Google Scholar

Copyright information

© Chapman & Hall 1993

Authors and Affiliations

  • Richard L. Smith
    • 1
  1. 1.Department of StatisticsUniversity of North CarolinaChapel HillUSA

Personalised recommendations