- Bowker, A. H. and Lieberman, G. K. (1972). Engineering Statistics. Prentice-Hall, Englewood Cliffs, New Jersey.Google Scholar
- Cox, D. C. (1982). An analytical method for uncertainty analysis of nonlinear output functions, with application to fault-tree analysis. IEEE Transactions on Reliability, R-31(5):265-68.Google ScholarCross Ref
- Cukier, R. I., Levine, H. B., and Shuler, K. E. (1978). Nonlinear sensitivity analysis of multiparameter model systems. Journal of Computational Physics, 26:1-42.Google ScholarCross Ref
- Downing, D. J., Gardner, R. H., and Hoffman, F. O. (1985). An examination of response-surface raethodologies for uncertainty analysis in assessment of models. Technometrics, 27(2):151-163.Google ScholarCross Ref
- Iman, R. L. and Conover, W. J. (1982). A dist6bution free approach to inducing rank correlation among input variables. Communications in Statistics--Simulation and Computation, B 11:311-334.Google Scholar
- lman, R. L. and Helton, J. C. (1988). An investigation of uncertainty and sensitivity analysis techniques for computer models. Risk Analysis, 8(1):71-90.Google ScholarCross Ref
- Iman, R. L., Helton, J. C., and Campbell, J. E. (1981a). An approach to sensitivity analysis of computer models: Part I--introduction, input variable selection and preliminary variable assessment. Journal of Quality Technology, 13(3):174-183.Google ScholarCross Ref
- lman, R. L., Helton, J. C., and Campbell, J. E. (1981b). An approach to sensitivity analysis of computer models: Part IImranking of input variables, response surface validation, distribution effect and technique synopsis. Journal of Quality Technology, 13(4):232-240.Google ScholarCross Ref
- McKay, M. D. (1978). A comparison of some sensitivity analysis techniques. Presented at the ORSA/TIMS annual meeting, New York.Google Scholar
- McKay, M. D. (1988). Sensitivity and uncertainty analysis using a statistical sample of input values. In Ronen, Y., editor, Uncertainty Analysis, chapter 4, pages 145- 186. CRC Press, Boca Raton, Florida.Google Scholar
- McKay, M. D., Beckman, R. J., Moore, L. M., and Picard, R. R. (1992). An alternative view of sensitivity in the analysis of computer codes. In Proceedings of the American Statistical Association Section on Physical and Engineering Sciences, Boston, Massachusetts.Google Scholar
- McKay, M. D., Conover, W. J., and Beckman, R. J. (1979). A comparison of three methods for selection values of input variables in the analysis of output from a computer code. Technometrics, 22(2):239-245.Google Scholar
- McKay, M. D., Conover, W. J., and Whiteman, D. E. (1976). Report on the application of statistical techniques to the analysis of computer codes. Technical Report LA-NURF_/3-6526-MS, Los Alamos National Laboratory, Los Alamos, NM.Google Scholar
- Morris, M. D. (1991). Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2):161-174. Google ScholarDigital Library
- Oblow, E. M. (1978). Sensitivity theory for reactor thermal-hydraulics problems. Nuclear Science and Engineering, 68:322-337.Google ScholarCross Ref
- Oblow, E. M., Pin, F. G., and Wright, R. Q. (1986). Sensitivity analysis using computer calculus: A nuclear waste isolation application. Nuclear Science and Engineering, 94:46-65.Google ScholarCross Ref
- Owen, A. B. (1992). Orthogonal arrays for computer integration and visualization. Statistica Sinica, 2(2).Google Scholar
- Pierce, T. H. and Cukier, R. I. (1981). Global nonlinear sensitivity analysis using Walsh functions. Journal of Computational Physics, 41:427-43.Google ScholarCross Ref
- Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989). Design and analysis of computer experiments. Statistical Science, 4(4):409-435.Google Scholar
- Saltelli, A. and Homma, T. (1992). Sensitivity analysis for a model output: Performance of black box techniques on three international benchmark exercises. Computational Statistics & Data Analysis, 13:73-94. Google ScholarDigital Library
- Saltelli, A. and Marivoet, J. (1990). Non-parametric statistics in sensitivity analysis for model output: A comparison of selected techniques. Reliability Engineering and System Safety, 28:229-53.Google ScholarCross Ref
- Stein, M. (1987). Large sample properties of simulations using Latin hypercube sampling. Technometrics, 29(2):143-151. Google ScholarDigital Library
- Taguchi, G. (1986). Introduction to Quality Engineering. Kraus International Publications, White Plains, New York.Google Scholar
- Tietjen, G. L. (1986). A Topical Dictionary of Statistics. Chapman and Hall, New York. Google ScholarDigital Library
- Wong, C. F. and Rabitz, H. (1991). Sensitivity analysis and principal component analysis in free energy calculations. Journal of Physics and Chemistry, 95:9628- 9630.Google ScholarCross Ref
Index Terms
- Latin hypercube sampling as a tool in uncertainty analysis of computer models
Recommendations
Progressive Latin Hypercube Sampling
Efficient sampling strategies that scale with the size of the problem, computational budget, and users needs are essential for various sampling-based analyses, such as sensitivity and uncertainty analysis. In this study, we propose a new strategy, ...
Confidence Intervals for Quantiles When Applying Latin Hypercube Sampling
SIMUL '10: Proceedings of the 2010 Second International Conference on Advances in System SimulationLatin hypercube sampling (LHS) is a variance-reduction technique (VRT) that can be thought of as an extension of stratified sampling in higher dimensions. It can also be considered a generalization of antithetic variates, another VRT. This paper ...
Optimizing the design of a latin hypercube sampling estimator
WSC '17: Proceedings of the 2017 Winter Simulation ConferenceStratified sampling and Latin hypercube sampling (LHS) reduce variance, relative to naïve Monte Carlo sampling, by partitioning the support of a random vector into strata. When creating these estimators, we must determine: (i) the number of strata; and, ...
Comments