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Book Reviews

REFERENCES

  • Brown, E.N., Kass, R.E., and Mitra, P.P. (2004), “Multiple Neural Spike Train Data Analysis: State-of-the-Art and Future Challenges,” Nature Neuroscience, 7, 456–461.
  • Dimatteo, I., Genovese, C.R., and Kass, R.E. (2001), “Bayesian Curve-Fitting with Free-Knot Splines,” Biometrika, 88, 1055–1071.
  • Kass, R.E., Ventura, V., and Brown, E.N. (2005), “Statistical Issues in the Analysis of Neuronal Data,” Journal of Neurophysiology, 94, 8–25.

REFERENCES

  • Cowell, R.G., Dawid, A.P., Lauritzen, S.L., and Spiegelhalter, D.J. (1999), Probabilistic Networks and Expert Systems, New York: Springer Verlag.
  • Koller, D., and Friedman, N. (2009), Probabilistic Graphical Models: Principles and Techniques. Adaptive Computation and Machine Learning, Cambridge, MA: MIT Press.
  • Neapolitan, R.E. (2003), Learning Bayesian Networks, Upper Saddle River, NJ: Prentice-Hall, Inc.
  • Pearl, J. (1988), Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, San Francisco, CA: Morgan Kaufmann.
  • Pourret, O., Naim, P., and Marcot, B. (2008), Bayesian Networks: A Practical Guide to Applications, Hoboken, NJ: Wiley.

REFERENCES

  • Felsenstein, J. (1981), “Evolutionary Trees from DNA Sequences: A Maximum Likelihood Approach,” Journal of Molecular Evolution, 17, 368–376.
  • Raftery, A.E., Newton, M.A., Satagopan, J.M., and Krivitsky, P.N. (2007), “Estimating the Integrated Likelihood via Posterior Simulation Using the Harmonic Mean Identity,” Bayesian Statistics, 8, 1–45.

REFERENCES

  • Berry, S.M., Carlin, B.P., Lee, J.J., and Muller, P. (2008), Bayesian Adaptive Methods for Clinical Trials, Boca Raton, FL: Chapman & Hall/CRC.
  • Chang, M. (2008), Adaptive Design Theory and Implementation Using SAS and R, Boca Raton, FL: Chapman & Hall/CRC.
  • Chow, S.C., and Chang, M. (2007), Adaptive Design Methods in Clinical Trials, Boca Raton, FL: Chapman & Hall/CRC.
  • Coffey, C.S., Levin, B., Clark, C., Timmerman, C., Wittes, J., Gilbert, P., and Harris, S. (2012), “Overview, Hurdles, and Future Work in Adaptive Designs: Perspectives from a National Institutes of Health-Funded Workshop,” Clinical Trials, 9, 671–680.
  • Food and Drug Administration (2010), “Guidance for Industry: Adaptive Design Clinical Trials for Drugs and Biologics,” available at: www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM201790.pdf.
  • Friedman, L.M., Furburg, C.D., and DeMets, D.L. (1998), Fundamentals of Clinical Trials, New York: Springer.
  • Gaydos, B., Anderson, K.M., Berry, D., Burnham, N., Chuang-Stein, C., Dudinak, J., Fardipour, P., Gallo, P., Givens, S., Lewis, R., Maca, J., and Pinheiro, J. (2009), “Good Practices for Adaptive Clinical Trials in Pharmaceutical Product Development,” Drug Information Journal, 43, 539–556.
  • Piantadosi, S. (2005), Clinical Trials: A Methodologic Perspective, Hoboken, NJ: Wiley.
  • Quinlan, J.A., and Krams, M. (2006), “Implementing Adaptive Designs: Logistical and Operational Considerations,” Drug Info Journal, 40, 437–444.
  • Quinlan, J., Gaydos, B., Maca, J., and Krams, M. (2010), “Barriers and Opportunities for Implementation of Adaptive Designs in Pharmaceutical Product Development,” Clinical Trials, 7, 167–173.
  • Ting, N. (ed.) (2010), Dose Finding in Drug Development, New York: Springer.

REFERENCES

  • Bernard, C. (1865/1957), An Introduction to the Study of Experimental Medicine, New York: Dover Edition; first published in 1865 (French).
  • Cronbach, L.J . (1982), “Prudent Aspirations for Social Inquiry,” in The Social Sciences, their Nature and Uses, eds. W.H. Kruskal, Chicago, IL: University of Chicago Press, pp. 61–82.
  • Feinberg, D.P. (2003), “A Short History of Randomized Experiments in Criminology: A Meager Feast,” Evaluation Review, 27, 218–227.
  • Feinberg, S.E. Singer, B.E, and Tanur, J.M. (1985), “Large Scale Social Experimentation in the United States,” in A Celebration of Statistics., eds. A.C. Atkinson and S.E. Feinberg, New York: Springer, pp. 287–326.
  • James Lind Library (2014), available at www.jameslindlibrary.org.
  • Larsen, O. (1992), Milestones and Millstones: Social Science at the National Science Foundation, 1945–1991, New Brunswick: Transaction Publishers.
  • Lide, D.R. (ed.) (2001), A Chronicle of Selected NBS/NIST Publications: 1901–2000. NIST Special Publications, Washington, DC: U.S. Department of Commerce, National Institute of Standards and Technology.
  • Marks, H. (1998), The Progress of Experiment: Science and Therapeutic Reform in the United States, 1900–1990, New York: Cambridge University Press.
  • Meier, P. (1972), “The Biggest Public Health Experiment Ever,” in Statistics: A Guide to the Unknown, eds. J. Tanur, F. Mosteller, W. H. Kruskal, R. F. Link, R. Pieters, G. R. Rising, and E. L. Lehmann, New York: Holden-Day, pp 2–13.
  • Mosteller, F., and Boruch, R. (eds.) (2002), Evidence Matters: Randomized Trials in Education Research, Washington, DC: Brookings Institution Press.
  • Riecken, H.W., Boruch, R.F., Campbell, D.T, Caplan, N., Glennan, T.K., Pratt, J.W., Rees, A., and Williams, W. (1974), Social Experimentation: A Method for Planning and Evaluating Social Intervention, New York: Academic Press.
  • Royal Statistical Society (2014/1887), Charter, available at: www.rss.org.
  • Schultz, T.W. (1982), “Distortions of Economic Research,” in The Social Sciences, their Nature and Uses. Papers Presented at the 50th Anniversary of the Social Science Research Building, University of Chicago December 16–18 1979, ed. W.H. Kruskal, Chicago, IL: University of Chicago Press, pp. 121–134.
  • Silverman, W.A. (1980), Retrolental Fibroplasia: A Modern Parable, New York: Grune and Stratton.
  • Strang, H. (2012), “Coalitions for a Common Purpose: Managing Relationships in Experiments,” Journal of Experimental Criminology, 8, 211–225.

REFERENCES

  • Faraway, J.J. (2005), Extending the Linear Model With R: Generalized Linear, Mixed Effects and Nonparametric Regression Models, Boca Raton, FL: Chapman and Hall.
  • Hastie, T.J., and Tibshirani, R.J. (1990), Generalized Additive Models, London: Chapman and Hall.
  • Park, M.Y., and Hastie, T.J. (2007), “L1-Regularization Path Algorithm for Generalized Linear Models,” Journal of the Royal Statistical Society, Series B, 69, 659–677.
  • Snyder, J.M., and Strömberg, D. (2010), “Press Coverage and Political Accountability,” Journal of Political Economy, 118, 355–408.
  • Wood, S.N. (2006), Generalized Additive Models: An Introduction With R, Boca Raton, FL: Chapman and Hall.

REFERENCE

REFERENCE

  • BIPM, IEC, IFCC, et. al. (1995), Guide to the Expression of Uncertainty in Measurement.

REFERENCES

  • Twyman, J. (2008), “Getting It Right: YouGov and Online Survey Research in Britain,” Journal of Elections, Public Opinion, and Parties, 18, 343–354.
  • Vavreck, L., and Rivers, D. (2008), “The 2006 Cooperative Congressional Election Study,” Journal of Elections, Public Opinion, and Parties, 18, 355–366.
  • Yeager, D.S., Krosnick, J.A., Chang, L., Javitz, H.S., Levendusky, M.S., Simpser, A., and Wang, R. (2011), “Comparing the Accuracy of RDD Telephone Surveys and Internet Surveys Conducted With Probability and Non-Probability Samples,” Public Opinion Quarterly, 75, 709–747.

REFERENCES

  • Faraway, J. (2014), Linear Models with R, Boca Raton, FL: Chapman and Hall/CRC.
  • Kolaczyk, E. (2009), Statistical Analysis of Network Data: Methods and Models, New York: Springer.

REFERENCES

  • Kendziorski, C., and Wang, P. (2006), “A Review of Statistical Methods for Expression Quantitative Trait Loci Mapping,” Mammalian Genome, 17, 509–517.
  • Li, S.Y., Williams, B., and Cui, Y.H. (2011), “A Combined p-value Approach to Infer Pathway Regulations in eQTL Mapping,” Statistics and Its Interface, 4, 389–402.
  • Rockman, M.V., and Kruglyak, L. (2006), “Genetics of Global Gene Expression,” Nature Reviews Genetics, 7, 862–872.
  • Siegmund, D., and Yakir, B. (2007), The Statistics of Gene Mapping, New York: Springer Verlag.
  • Wu, R., Ma, C.-X., and Casella, G. (2007), Statistical Genetics of Quantitative Traits: Linkage, Maps and QTL, New York: Springer-Verlag.
  • Yandell, B.S., Mehta, T., Banerjee, S., Shriner, D., Venkataraman, R., Moon, J.Y., Neely, W.W., Wu, H., von Smith, R., and Yi, N. (2007), “R/qtlbim: QTL With Bayesian Interval Mapping in Experimental Crosses,” Bioinformatics, 23, 641–643.

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