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Collected Reading List and Further Readings:

Foundations; Statistical Models

  1. Feynman, R., 1974. CalTech Commencement Address,
    http://calteches.library.caltech.edu/51/2/CargoCult.htm

  2. Freedman, D.A., and D. Lane, 1983. A Nonstochastic Interpretation of Reported Significance Levels, Journal of Business & Economic Statistics, 1, 292-298.

  3. Freedman, D.A., 1995. Some issues in the foundations of statistics, Foundations of Science, 1, 19--39. https://doi.org/10.1007/BF00208723

  4. Freedman, D.A., 1999. From association to causation: some remarks on the history of statistics, Statistical Science, 14(3), 243--258.

  5. Freedman, D.A., and R. Berk, 2001. Statistical Assumptions as Empirical Commitments,
    http://escholarship.org/uc/item/0zj8s368#page-1 (also in Freedman, D.A., 2010. Statistical Models and Causal Inference: A dialog with the Social Sciences, Cambridge University Press. D. Collier, J. Sekhon, P.B. Stark, eds.)

  6. Freedman, D.A., 2008. On types of scientific inquiry: the role of qualitative reasoning, The Oxford Handbook of Political Methodology, Box-Steffensmeier, J.M., H.E. Brady, and D. Collier (eds), Oxford University Press, Oxford. DOI: 10.1093/oxfordhb/9780199286546.003.0012. Preprint

  7. Freedman, D.A., 2009. Statistical Models: Theory and Practice, 2nd edition, Cambridge University Press.

  8. Freedman, D.A., R. Pisani, and R. Purves, 2007. Statistics, 4th edition, W.W. Norton, New York.

  9. Jewell, N.P., J.A. Lewnard, and B.L. Jewell, 2020. Predictive Mathematical Models of the COVID-19 Pandemic: Underlying Principles and Value of Projections, JAMA, 323(19):1893-1894. doi:10.1001/jama.2020.6585

  10. Klemes, V., 1989. The Improbable Probabilities of Extreme Floods and Droughts, in O. Starosolsky and O.M. Meldev (eds), Hydrology and Disasters, James and James, London, 43--51.
    https://www.itia.ntua.gr/en/getfile/1107/1/documents/1997_ImprobProbabilities_OCR.pdf

  11. LeCam, L., 1977. Note on metastatistics or 'An essay toward stating a problem in the doctrine of chances,' Synthese, 36, 133-160.

  12. Stark, P.B., 1997. SticiGui

  13. Stark, P.B., 2016a. Pay no attention to the model behind the curtain

  14. Stark, P.B., 2016b. The value of P-values, The American Statistician, 70, DOI:10.1080/00031305.2016.1154108

  15. Stark, P.B., 2017. Mathematical Foundations, Inequalities, Statistical models, Introduction to permutation tests, Rabbits and Cargo-Cult Statistics, Generating pseudo-random samples and permutations Combining tests

  16. Stark, P.B., and A. Saltelli, 2018. Cargo-cult Statistics and Scientific Crisis, Significance, 15(4), 40--43. https://www.significancemagazine.com/593

Statistical methodology

  1. Freedman, D.A., 2008. Randomization does not justify logistic regression, Statistical Science, 23 237--249. DOI: 10.1214/08-STS262 https://arxiv.org/pdf/0808.3914.pdf

  2. Hastie, T., R. Tibshirani, and J. Friedman, 2009. Elements of Statistical Learning: Data Mining, Inference, and Prediction. Second Edition, Springer-Verlag, NY. https://web.stanford.edu/~hastie/Papers/ESLII.pdf

  3. McCullagh, P. and J.A. Nelder, 1983. Generalized Linear Models, 2nd edition, Chapman & Hall, NY.

Evidence, Models, and Public Policy

  1. Saltelli, A., P.B. Stark, W. Becker, and P. Stano, 2015. Climate Models as Economic Guides: Scientific Challenge or Quixotic Quest?, Issues in Science and Technology, Spring 2015. Reprint: http://www.stat.berkeley.edu/~stark/Preprints/saltelliEtal15.pdf

  2. van der Sluijs, J.P., A.C. Petersen, P.H.M. Janssen, J.S. Risbey, and J.R. Ravetz, 2008. Exploring the quality of evidence for complex and contested policy decisions, Environmental Research Letters, 3, doi:10.1088/1748-9326/3/2/024008

  3. van der Sluijs, J.P., 2016. Numbers Running Wild, Chapter 5 in The Rightful Place of Science: Science on the Verge, A. Benessia, S. Funtowicz, M. Giampietro, Â.G. Pereira, J. Ravetz, A. Saltelli, R. Strand, J.P. van der Sluijs, eds., Consortium for Science, Policy & Outcomes, AZ & DC. http://www.andreasaltelli.eu/file/repository/Science_on_the_Verge_FINAL_.pdf

Foundations: Computation, Optimization

  1. Goldberg, D., 1991. What every computer scientist should know about floating-point arithmetic, ACM Computing Surveys, 23, 5--48.

  2. Ottoboni, K. and P.B. Stark, 2018. Random problems with R, ArXiV, https://arxiv.org/abs/1809.06520. also see https://stat.ethz.ch/pipermail/r-devel/2018-September/076817.html

  3. Stark, P.B., and K. Ottoboni, 2018. Random sampling: practice makes imperfect, https://arxiv.org/abs/1810.10985

Earthquake probabilities

  1. USGS 2008 Bay Area Earthquake Probabilities.
    http://earthquake.usgs.gov/regional/nca/ucerf/

  2. Cornell, C.A., 1968. Engineering seismic risk analysis, Bull. Seism. Soc. Am, 58, 1583--1606.

  3. Mulargia, F., P.B. Stark, and R.J. Geller, 2017. Why is probabilistic seismic hazard analysis (PSHA) still used? Physics of the Earth and Planetary Interiors, 264, 63-75. https://doi.org/10.1016/j.pepi.2016.12.002

  4. Stark, P.B. and D.A. Freedman, 2003. What is the Chance of an Earthquake? in Earthquake Science and Seismic Risk Reduction, F. Mulargia and R.J. Geller, eds., NATO Science Series IV: Earth and Environmental Sciences, v. 32, Kluwer, Dordrecht, The Netherlands, 201–213. Preprint: http://www.stat.berkeley.edu/~stark/Preprints/611.pdf

Impact of Climate Change

  1. Houser, T., R. Kopp, S. Hsiang, M. Delgado, A. Jina, K. Larsen, M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising, and P. Wilson, 2014. The American Climate Prospectus: Economic Risks in the United States, 2014. http://rhg.com/reports/climate-prospectus

  2. Hsiang, S., R. Kopp, A. Jina, J. Rising, M. Delgado, S. Mohan, D.J. Rasmussen, R. Muir-Wood, P. Wilson, M. Oppenheimer, K. Larsen, and T. Houser, 2017. Estimating economic damage from climate change in the United States, Science, 356, 1362-1369 DOI: 10.1126/science.aal4369

  3. Ranson, M., 2014. Crime, weather, and climate change, Journal of Environmental Economics and Management, 67(3), 274-302. https://doi.org/10.1016/j.jeem.2013.11.008

  4. Urban, M.C., 2015. Accelerating extinction risk from climate change, Science, 348, Issue 6234, 571--573, DOI: 10.1126/science.aaa4984, http://science.sciencemag.org/content/348/6234/571.full

Reproducibility and Scientific Method

  1. Ball, P., 2018. High-profile journals put to reproducibility test, Nature, 27 August, https://www.nature.com/articles/d41586-018-06075-z, doi: 10.1038/d41586-018-06075-z

  2. Barba, L., 2016. The hard road to reproducibility, Science, 354, 142. doi 10.1126/science.354.6308.142

  3. Barba, L., 2016. Reproducibility Syllabus, http://lorenabarba.com/blog/barbagroup-reproducibility-syllabus/

  4. Barba, L., 2018. Terminologies for Reproducible Research, https://arxiv.org/abs/1802.03311

  5. J.B. Buckheit and D.L. Donoho, 1995. Wavelab and Reproducible Research, https://statweb.stanford.edu/~wavelab/Wavelab_850/wavelab.pdf

  6. Implementing Reproducible Research, edited by V. Stodden, F. Leisch and R. Peng

  7. The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences Kitzes, Turek and Deniz, eds.

  8. Reproducibility: a Primer on Semantics and Implications for Research

  9. Mesnard, O., and L.A. Barba, 2017. Reproducible and replicable CFD: it's harder than you think, IEEE/AIP Computing in Science and Engineering, 19(4):44--55. 10.1109/MCSE.2017.3151254 Preprint: arXiv:1605.04339

  10. Rokem, A., B. Marwick, and V. Staneva, 2018. Assessing Reproducibility, in The Practice of Reproducible Research: Case Studies and Lessons from the Data-Intensive Sciences, University of California Press. https://www.practicereproducibleresearch.org/core-chapters/2-assessment.html

  11. Schapin, and Schaffer, 1985. Leviathan and the Air-Pump: Hobbes, Boyle, and the Experimental Life, Princeton University Press, NJ ISBN 0-691-08393-2

  12. Stark, P.B., 2018. No Reproducibility Without Preproducibility, Nature, 557, 613. https://www.nature.com/magazine-assets/d41586-018-05256-0/d41586-018-05256-0.pdf doi: 10.1038/d41586-018-05256-0