Bayesian analysis made simple: An excel GUI for winBUGS

Bayesian Analysis Made Simple: An Excel GUI for WinBUGS

Volumn , Issue , 2016, Pages 1-342

  Woodward, Phil ^a  

^a Pfizer RandD   (United Kingdom)

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EID: 85051689963     PISSN: None     EISSN: None     Source Type: Book    
DOI: None     Document Type: Book

References (98)

1. Agresti, A. (2007). An Introduction to Categorical Data Analysis. 2nd Edn. John Wiley & Sons, Inc., New York.
2. Baker, S.G. (1995). The multinomial-Poisson transformation. The Statistician 43, 495-504.
3. Baker, W.L., Baker, E.L., and Coleman, C.I. (2009). Pharmacologic treatments for chronic obstructive pulmonary disease: A mixed-treatment comparison meta-analysis. Pharmacotherapy 29(8), 891-905.
4. Bartlett, M.S. (1936). The square-root transformation in analysis of variance. Supplement to the Journal of the Royal Statistical Society 3(1), 68-78.
5. Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society (B) 57, 289-300.
6. Berger, J. (2006). The case for objective Bayesian analysis. Bayesian Analysis 1(3), 385-402.
7. Bliss, C.I. (1935). The calculation of the dosage-mortality curve. Annals of Applied Biology 22, 134-167.
8. Bolstad, W.M. (2007). Introduction to Bayesian Statistics. 2nd Edn. John Wiley & Sons, Inc., New York.
9. Box, G.E.P. (1983). An apology for ecumenism in statistics, in Scientific Inference, Data Analysis, and Robustness. Academic Press, New York, pp. 51-84.
10. Box, G.E.P. (2000). Box on Quality and Discovery: With Design, Control and Robustness. John Wiley & Sons, Inc., New York.
11. Box, G.E.P. and Bisgaard, S. (1987). The scientific context of quality improvement. Quality Progress 4(2), 54-62.
12. Box, G.E.P., Hunter, W.G., and Hunter, J.S. (1978). Statistics for Experimenters. John Wiley & Sons, Inc., New York.
13. Box, G.E.P. and Meyer, R.D. (1993). Finding the active factors in fractioned screening experiments. Journal of Quality Technology 25, 94-105.
14. Box, G.E.P. and Tiao, G.C. (1968). A Bayesian approach to some outlier problems. Biometrika 55(1), 119-129.
15. Box, G.E.P. and Tiao, G.C. (1973). Bayesian Inference in Statistical Analysis. Wiley Classics, New York.
16. Brooks, S.P. and Gelman, A. (1998). Alternative methods for monitoring convergence of iterative simulations. Journal of Computational and Graphical Statistics 7, 434-455.
17. Brown, H. and Prescott, R. (2006). Applied Mixed Models in Medicine. 2nd Edn. John Wiley & Sons Ltd., New York.
18. Chipman, H., Hamada, M., and Wu, C.F.J. (1997). A Bayesian variable-selection approach for analyzing designed experiments with complex aliasing. Technometrics 39(4), 372-381.
19. Clarke, G.M. and Cooke, D. (2004). A Basic Course in Statistics. 5th Edn. Hodder Arnold, London, U.K.
20. Cochran, W.G. and Cox, G.M. (1992). Experimental Designs. 2nd Edn. Wiley Classics Library. John Wiley & Sons, Inc., New York.
21. Collaborative Group.. (1993). Meta-analysis of randomised controlled trials of selective decontamination of the digestive tract. British Medical Journal 307, 525-532. (Selective Decontamination of the Digestive Tract Trialists' Contamination Group.)
22. Collett, D. (2003). Modelling Survival Data in Medical Research. Chapman & Hall/CRC, Boca Raton, FL.
23. Cornell, J.A. (2002). Experiments with Mixtures: Designs, Models and the Analysis of Mixture Data. 3rd Edn. John Wiley & Sons, Inc., New York.
24. Cox, D.R. (1958). Planning of Experiments. John Wiley & Sons, Inc., New York.
25. Davies, O. (1954). The Design and Analysis of Industrial Experiments. John Wiley & Sons, Inc., New York.
26. Davis, T.P. (1995). Analysis of an experiment aimed at improving the reliability of transmission centre shafts. Lifetime Data Analysis 1, 275-306.
27. Diggle, P.J., Heagerty, P., Liang, K.Y., and Zeger, S.L. (2002). Analysis of Longitudinal Data. 2nd Edn. Oxford University Press, New York.
28. Dobson, A.J. (1988). An Introduction to Statistical Modelling. Chapman & Hall, New York.
29. Dobson, A.J. (2002). An Introduction to Generalized Linear Models. 2nd Edn. Chapman & Hall/CRC, Boca Raton, FL.
30. Draper, D. (1995). Assessment and propagation of model uncertainty (with discussion). Journal of the Royal Statistical Society, B 57(1), 45-97.
31. Draper, N. and Smith, H. (1998). Applied Regression Analysis. 3rd Edn. John Wiley & Sons, Inc., New York.
32. Edwards, A.W.F. (1992). Likelihood. Expanded Edition. The John Hopkins University Press, Baltimore, MD.
33. Faraway, J.J. (2004). Linear Models with R. Chapman & Hall/CRC, Boca Raton, FL.
34. Faraway, J.J. (2006). Extending the Linear Model with R. Chapman & Hall/CRC, Boca Raton, FL.
35. Fisher, R.A. (1925). Statistical Methods for Research Workers. Oliver and Boyd, Edinburgh, Scotland.
36. Fitzmaurice, G.M., Laird, N.M., and Ware, J.H. (2004). Applied Longitudinal Analysis. John Wiley & Sons, Inc., Hoboken, NJ.
37. Fleiss, J.L. (1986). The Design and Analysis of Clinical Experiments. John Wiley & Sons, Inc., New York.
38. Gelfand, A.E., Dey, D.K., and Chang, H. (1992). Model determination using predictive distributions with implementation via sampling-based methods, in Bayesian Statistics 4 (eds. J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith). Oxford University Press, Oxford, U.K., pp. 147-168.
39. Gelfand, A.E., Hills, S.E., Racine-Poon, A., and Smith, A.F.M. (1990). Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Society 85, 972-985.
40. 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.
41. Gelman, A. (2005). Analysis of variance-Why it is more important than ever (with discussion). Annals of Statistics 33, 1-53.
42. Gelman, A. (2006). Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1(3), 515-553.
43. Gelman, A., Carlin, J., Stern, H., and Rubin, D. (2004). Bayesian Data Analysis. 2nd Edn. CRC Press/Chapman & Hall, Boca Raton, FL.
44. Gelman, A. and Rubin, D. (1992). Inference from iterative simulation using multiple sequences. Statistical Science 7, 457-511.
45. Grieve, A.P. (1987). Applications of Bayesian software: Two examples. Statistician 36, 283-288.
46. Hand, D. (1981). Discrimination and Classification. Wiley, Chichester, U.K.
47. Henkin, E. (1986). The reduction of variability of blood glucose levels, in Fourth Supplier Symposium on Taguchi Methods. American Supplier Institute, Dearborn, MI, pp. 758-785.
48. Higgins, J.P.T., Thompson, S.G., and Spiegelhalter, D.J. (2009). A re-evaluation of random-effects meta-analysis. Journal of the Royal Statistical Society (A) 172, 137-159.
49. Hill, A.V. (1910). The possible effect of the aggregation of molecules of haemoglobin on its dissociation curves. Proceedings of the Physiological Society 40, iv-vii.
50. Jackman, S. (2009). Reagan dataset. http://jackman.stanford.edu/mcmc/index.php (last updated December 12, 2010; accessed May 15, 2011).
51. Jones, B. and Kenward, M.G. (2003). Design and Analysis of Cross-Over Trials. 2nd Edn. Chapman & Hall/CRC, Boca Raton, FL.
52. Kuo, L. and Mallick, B. (1998). Variable selection for regression models. Sankyha B 60, 65-81.
53. Lawson, J. (2008). Bayesian interval estimates of variance components used in quality improvement studies. Quality Engineering 20, 334-345.
54. Lee, P.M. (2004). Bayesian Statistics: An Introduction. 3rd Edn. Hodder Arnold, London, U.K.
55. Lindley, D.V. (1985). Making Decisions. 2nd Edn. John Wiley & Sons, Inc., New York.
56. Lindley, D.V. and Smith, A.F.M. (1972). Bayes estimates for the linear model (with discussion). Journal of the Royal Statistical Society (B) 34, 1-42.
57. Lunn, D.J., Thomas, A., Best, N., and Spiegelhalter, D. (2000) WinBUGS-A Bayesian modelling framework: Concepts, structure, and extensibility. Statistics and Computing 10, 325-337.
58. Margolese, M.S. (1970). Homosexuality: A new endocrine correlate. Hormones and Behavior 1, 151-155.
59. Marshall, E.C. and Spiegelhalter, D.J. (2007). Identifying outliers in Bayesian hierarchical models: A simulation-based approach. Bayesian Analysis 2(2), 409-444.
60. McCullagh, P. and Nelder, J. (1989). Generalized Linear Models. 2nd Edn. Chapman & Hall, London, U.K.
61. McCulloch, C.E. and Searle, S.R. (2001). Generalized, Linear and Mixed Models. John Wiley & Sons, Inc., New York.
62. McDonald, G. and Schwing, R.C. (1973). Instabilities of regression estimates relating to air pollution and mortality. Technometrics 15, 463-481.
63. Meyer, R.D. and Wilkinson, R.G. (1998). Bayesian variable assessment. Communications in Statistics-Theory and Methods 27(11), 2675-2705.
64. Miller, A.E., Thompson, W.L., Mortenson, D.C., and Moore, C. (2010). Protocol for ground-based monitoring of vegetation in the Southwest Alaska Network. Natural Resource Report NPS/SWAN/NRR-2010/205. National Park Service, Fort Collins, CO. Available from the Natural Resource Publications Management website: http://www.nature.nps.gov/publications/NRPM
65. Mood, A.M., Graybill, F.A., and Boes, D.C. (1974). Introduction to the Theory of Statistics. 3rd Edn. McGraw-Hill, Inc., New York.
66. Myers, R., Montgomery, D., and Vining, G. (2002). Generalized Linear Models: With Applications in Engineering and Sciences. John Wiley & Sons, Inc., New York.
67. Neal, R. (1998). Suppressing random walks in Markov chain Monte Carlo using ordered over-relaxation, in Learning in Graphical Models (ed. M.I. Jordan). Kluwer Academic Publishers, Dordrecht, the Netherlands, pp. 205-230.
68. Nelder, J.A. (1977). A reformulation of linear models (with discussion). Journal of the Royal Statistical Society (A) 140, 48-77.
69. Nelder, J. and Wedderburn, R. (1972). Generalized linear models. Journal of the Royal Statistical Society, Series A 132, 370-384.
70. Ntzoufras, I. (2009). Bayesian Modeling Using WinBUGS. John Wiley & Sons, Inc., Hoboken, NJ.
71. O'Hagan, A., Buck, C.E., Daneshkhah, A., Eiser, J.R., Garthwaite, P.H., Jenkinson, D.J., Oakley, J.E., and Rakow, T. (2006). Uncertain Judgements. Eliciting Experts' Probabilities. John Wiley & Sons Ltd., New York.
72. O'Hara, R.B. and Sillanpaa, M.J. (2009). A review of Bayesian variable selection methods: What, how and which. Bayesian Analysis 4(1), 85-118.
73. Plummer, M. (2010). JAGS: Just Another Gibbs Sampler. http://calvin.iarc.fr/~martyn/software/jags/ (accessed May 15, 2011).
74. Prentice, R.L. (1976). A generalization of the probit and logit methods for dose response curves. Biometrics 32, 761-768.
75. Raiffa, H. (1968). Decision Analysis. McGraw-Hill, Inc., New York.
76. Robinson, T.J., Anderson-Cook, C.M., and Hamada, M.S. (2009). Bayesian analysis of split-plot experiments with nonnormal responses for evaluating nonstandard performance criteria. Technometrics 51(1), 56-64.
77. Robinson, T.J., Myers, R.H., and Montgomery, D.C. (2004). Analysis considerations in industrial split-plot experiments with nonnormal responses. Journal of Quality Technology 36, 180-192.
78. Rosenbaum, P.R. (2009). Design of Observational Studies. Springer, New York.
79. Senn, S. (2002). Cross-Over Trials in Clinical Research. 2nd Edn. John Wiley & Sons, Ltd., New York.
80. Senn, S. (2007). Statistical Issues in Drug Development. 2nd Edn. John Wiley & Sons, Ltd., New York.
81. Shafer, J. (1982). Lindley's paradox (with discussion). Journal of the American Statistical Association 77, 325-334.
82. Short, T. (2004). R reference card. http://cran.r-project.org/doc/contrib/Short-refcard.pdf (accessed May 15, 2011).
83. Smith, A.F.M. (1986). Some Bayesian thoughts on modelling and model choice. The Statistician 35, 97-102.
84. Smith, T.C., Spiegelhalter, D.J., and Thomas, A. (1995). Bayesian approaches to random-effects meta-analysis: A comparative study. Statistics in Medicine 14, 2685-2699.
85. Spiegelhalter, D., Abrams, K., and Myles, J. (2004). Bayesian Approaches to Clinical Trials and Health-Care Evaluation. John Wiley & Sons, New York.
86. Spiegelhalter, D., Best, N., Carlin, B., and van der Linde, A. (2002). Bayesian measures of model complexity and fit (with discussion). Journal of the Royal Statistical Society, Series B 64, 583-640.
87. Spiegelhalter, D., Thomas, A., Best, N., and Gilks, W. (1996). BUGS 0.5 Manual (Version ii). MRC Biostatistics Unit, Cambridge, U.K.
88. Spiegelhalter, D., Thomas, A., Best, N., and Lunn, D. (2003). WinBUGS User Manual, Version 1.4. MRC Biostatistics Unit, Cambridge, U.K.
89. Thall, P.F. and Vail, S.C. (1990). Some covariance models for longitudinal count data with overdispersion. Biometrics 46, 657-671.
90. Tierney, L. (1994). Markov chains for exploring posterior distributions. Annals of Statistics 22, 1701-1762.
91. Venables, W.N., Smith, D.M., and the R Development Core Team. (2010). An introduction to R (v2.11.1). http://cran.r-project.org/doc/manuals/R-intro.pdf (accessed May 15, 2011).
92. Verzani, J. (2005). Using R for Introductory Statistics. Chapman & Hall/CRC, Boca Raton, FL.
93. Walkenbach, J. (2007). Excel 2007 Charts. Wiley Publishing Inc., New York.
94. Wolstenholme, L.C. (1999). Reliability Modelling: A Statistical Approach. Chapman & Hall/CRC, Boca Raton, FL.
95. Woodward, P. (2005). BugsXLA. Bayes for the common man. Journal of Statistical Software 14, 5. http://www.jstatsoft.org/v14/i05
96. Woodward, P. and Walley, R. (2009). Bayesian variable selection for fractional factorial experiments with multilevel categorical factors. Journal of Quality Technology 41(3), 228-240.
97. Wu, C.F.J. and Hamada, M. (2000). Experiments. Planning, Analysis and Parameter Design Optimization. John Wiley & Sons, Inc., New York.
98. Zeger, S.L. and Qaqish, B. (1988). Markov regression models for time series: A quasi-likelihood approach. Biometrics 44, 1019-1031.