Graduate Student Reading List
The Duke Statistics PhD program does not feature required courses or a rigid curriculum; students' varied backgrounds and interests are accommodated by programs tailored to their needs. The following books have been recommended by various Duke Statistics faculty members for students wishing to explore unfamiliar areas of statistics, or deepen their knowledge about familiar ones. This is not a "required reading list,'' and no student is expected to have a detailed familiarity with all of these sources. However, a basic knowledge of essential ideas contained therein is expected and appropriate for doctoral candidates. Students are also encouraged to look at current journals from statistics (especially JASA, JRSS-B, Biometrika, perhaps the Annals of Statistics, Biometrics or Biostatistics, and, for review articles and gentle introductions, Statistical Science) and journals from other fields (mathematics, computing, philosophy of science, and anything else that interests you). Many articles in medical journals (JAMA, NEJM) and journals in the social and empirical sciences include interesting data sets and use conventional statistical methods; these are a good source of examples for illustrating and experimenting with more novel statistical methods developed in your research.
GENERAL STATISTICS TEXTS
- A.F.M. Smith and J.M. Bernardo, Bayesian Theory.
- J.O. Berger, Statistical Decision Theory and Bayesian Analysis.
- P. Bickel and K. Doksum, Mathematical Statistics: Basic Ideas and Selected Topics.
- M.H. DeGroot, Optimal Statistical Decisions.
- A. Gelman, J.B. Carlin, H.S. Stern and D.B. Rubin, Bayesian Data Analysis. (2nd edition)
- D.V. Lindley, Introduction to Probability and Statistics, from a Bayesian viewpoint (2 Vols).
- S.J. Press, Applied Multivariate Analysis (from Bayesian and Frequentist Perspectives).
- A. O'Hagan, Kendall's Advanced Theory of Statistics, Vol 2b: Bayesian Inference.
- C. Robert, Bayesian Statistical Analysis.
- L. Wasserman, All of Statistics.
REGRESSION AND MODELING
- S.F. Arnold, The Theory of Linear Models and Multivariate Analysis.
- D.M. Bates and D.G. Watts, Nonlinear Regression and its Applications.
- Y. Bishop, S. Fienberg and P. Holland, Discrete Multivariate Analysis.
- G.E.P. Box, W.G. Hunter and J.S. Hunter, Statistics for Experimenters.
- J. Chambers and T. Hastie, Statistical Models in S.
- D.R. Cox, Planning of Experiments.
- P. Diggle, Time Series: A Biostatistical Introduction.
- N.R. Draper and H. Smith, Applied Regression Analysis.
- T.J. Hastie and R.J. Tibshirani, Generalized Additive Models.
- J.D. Kalbfleisch and R.L. Prentice, The Statistical Analysis of Failure Time Data.
- P. McCullagh and J. Nelder, Generalized Linear Models.
- G.A.F. Seber, Linear Regression Analysis.
- A. Stuart and J.K. Ord, Kendall's Advanced Theory of Statistics, Vol 2a: Classical Inference and Relationship.
- W.N. Venables and B.D. Ripley, Modern Applied Statistics with S-Plus.
- S. Weisberg, Applied Linear Regression.
- M. West and P.J. Harrison, Bayesian Forecasting and Dynamic Models.
- M. West, A. Pole, and P.J. Harrison, Applied Bayesian Forecasting and Time Series Analysis.
- L. Wasserman, All of Statistics.
- A. Agreati, Categorical Data Analysis. (2nd Edition)
- N.A.C. Cressie, Statistics for Spatial Data.
- S. Banerjee, B.P. Carlin & A.C. Gelfand, Hierarchical Modeling and Analysis for Spatial Data.
- P. Congdon, Applied Bayesian Modeling.
PROBABILITY AND STOCHASTIC PROCESSES
- P. Billingsley, Probability and Measure.
- L. Breiman, Probability Theory.
- K.L. Chung, A Course in Probability Theory.
- D.R. Cox and H.D. Miller, The Theory of Stochastic Processes.
- S. Karlin and J. Taylor, First Course On Stochastic Processes.
- S. Karlin and J. Taylor, Second Course On Stochastic Processes.
FURTHER DECISION THEORY AND FOUNDATIONS
- V. Barnett, Comparative Statistical Inference.
- D. Basu, Statistical Information and Likelihood.
- J.O. Berger and R.L. Wolpert, The Likelihood Principle (2nd edn).
- S. French, Decision Theory.
- J.A. Hartigan, Bayes Theory.
- C. Howson and P. Urbach, Scientific reasoning: the Bayesian approach.
- D.M. Kreps, Notes on the Theory of Choice.
- D.V. Lindley, Making Decisions.
- E. Nagel, The Structure of Science.
- H. Raiffa and R. Schlaifer, Applied Statistical Decision Theory.
- L.J. Savage, The Foundations of Statistics.
- J.Q. Smith, Decision Analysis, a Bayesian Approach.
- R. Royall, Statistical Evidence.
STATISTICAL COMPUTING
- R.A. Becker, J.M. Chambers and A.R. Wilks, The New S Language.
- J.M. Chambers and T.J. Hastie, Statistical Models in S.
- W.R. Gilks, S. Richardson and D.J. Spiegelhalter, Markov Chain Monte Carlo in Practice.
- B. Ripley, Stochastic Simulation.
- M. Tanner, Tools for Statistical Inference.
- R.A. Thisted, Elements of Statistical Computing.
- L. Devroye, Non-Uniform Random Variate Generation. (2nd Edition - free online)
- J. Liu, Monte Carlo Strategies in Scientific Computing.
- G. Casella and C. Robert Cassella, Monte Carlo Statistical Methods.
- G. Fishman, Monte Carlo: Concepts, Algorithms and Application.
OTHERS
- T. Santner, The Design and Analysis of Computer Experiments.
- D. Berry and D. Stangl (eds), Bayesian Biostatistics.
- R.A. Fisher, Statistical Methods and Scientific Inference.
REFERENCES AND COMPENDIA
- M. Abramowitz and I. Stegun, Handbook of Mathematical Functions.
- N.L. Johnson and S. Kotz, Distributions in Statistics (Vols 1--4).