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Articles

Bayesian Modeling and Inference for One-Shot Experiments

ORCID Icon &
Pages 55-64 | Received 25 Nov 2022, Accepted 29 May 2023, Published online: 24 Jul 2023

References

  • Casella, G., and Berger, R. L. (2002), Statistical Inference (2nd ed.), Pacific Grove, CA: Duxbury.
  • de Boor, C. (2001), A Practical Guide to Splines (revised ed.), New York: Springer.
  • Dror, H. A., and Steinberg, D. M. (2008), “Sequential Experimental Designs for Generalized Linear Models,” Journal of the American Statistical Association, 103, 288–297. DOI: 10.1198/016214507000001346.
  • Efron, B., and Hastie, T. (2016), Computer Age Statistical Inference, New York, NY: Cambridge University Press. Available at https://hastie.su.domains/CASI/.
  • Eilers, P. H. C., and Marx, B. D. (1996), “Flexible Smoothing with b-splines and Penalties,” Statistical Science, 11, 89–102. With discussion and rejoinder, 102–121. DOI: 10.1214/ss/1038425655.
  • Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2014), Bayesian Data Analysis (3rd ed.), Boca Raton, FL: Chapman and Hall/CRC. Available at http://www.stat.columbia.edu/∼gelman/book/.
  • Hickey, G., and Hart, A. (2013), “Statistical Aspects of Risk Characterisation in Ecotoxicology,” in Risk and Uncertainty Assessment for Natural Hazards, eds. J. C. Rougier, R. S. J. Sparks, and L. J. Hill, Cambridge, UK: Cambridge University Press.
  • Langlie, H. J. (1963), “A Reliability Test Method for ‘One-Shot’ Items,” Technical Report ADP014612, Aeronutronic Division, Ford Motor Company. Available at https://apps.dtic.mil/sti/citations/ADP014612.
  • Neelon, B., and Dunson, D. B. (2004), “Bayesian Isotonic Regression and Trend Analysis,” Biometrics, 60, 398–406. DOI: 10.1111/j.0006-341X.2004.00184.x.
  • Neyer, B. T. (1992), “An Analysis of Sensitivity Tests,” Technical Report, Mound, Miamisburg, OH, US. Available at https://www.osti.gov/biblio/5654343, OSTI identifier 5654343.
  • Neyer, B. T. (1994), “A D-optimality-based Sensitivity Test,” Technometrics, 36, 61–70.
  • Pya, N., and Wood, S. N. (2015), “Shape Constrained Additive Models,” Statistics and Computing, 25, 543–559. DOI: 10.1007/s11222-013-9448-7.
  • R Core Team. (2020), R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. Available at https://www.R-project.org/.
  • STANAG 4560. (2016), Electro-Explosive Devices, Assessment and Test Methods for Characterization (3rd ed.). Full reference number NS0/1418(2016)SGA/4560, 21 November 2016.
  • Steinberg, D. M., Dror, H., and Villa, L. (2014), “Discussion of “Three-phase Optimal Design of Sensitivity Experiments” by Wu and Tian,” Journal of Statistical Planning and Inference, 149, 26–29. DOI: 10.1016/j.jspi.2013.12.012.
  • Teller, A., Steinberg, D. M., Teper, L., Rozenblum, R., Mendel, L., and Jaeger, M. (2016), “New Methods for Sensitivity Tests of Explosive Devices,” in 13th International Conference on Probabilistic Safety Assessment and Management (PSAM 13), Seoul, Korea.
  • Wetherill, G. B. (1963), “Sequential Estimation of Quantal Response Curves,” Journal of the Royal Statistical Society, Series B, 25, 1–48. DOI: 10.1111/j.2517-6161.1963.tb00481.x.
  • Wood, S. N. (2017), Generalized Linear Models: An Introduction with R (2nd ed.), Boca Raton FL: CRC Press.
  • Wu, C. F. J., and Tian, Y. (2014), “Three-Phase Optimal Design of Sensitivity Experiments,” Journal of Statistical Planning and Inference, 149, 1–15. With comments and rejoinder, pp. 16–32. DOI: 10.1016/j.jspi.2013.10.007.
  • Young, L. J., and Easterling, R. G. (1994), “Estimation of Extreme Quantiles based on Sensitivity Tests: A Comparative Study,” Technometrics, 36, 48–60. DOI: 10.1080/00401706.1994.10485400.