Susan M. Sanchez
- Banks, J., J. S. Carson and B. L. Nelson. 1996. Discrete-event system simulation, 2nd ed. Englewood Cliffs, New Jersey: Prentice-Hall.Google Scholar
- Bechhofer, R. E. 1954. A single-sample multiple decision procedure for ranking means of normal populations. Annals of Mathematical Statistics 25:16- 29.Google ScholarCross Ref
- Bechhofer, R. E., C. W. Dunnett and M. Sobel. 1954. A two-sample multiple decision procedure for ranking means of normal populations with a common unknown variance. Biometrika 41:170- 176.Google Scholar
- Bechhofer, R. E., S. Elmaghraby and N. Morse. 1959. A single-stage multiple decision procedure for selecting the multinomial event which has the highest probability. Annals of Mathematical Statistics 30:102-119.Google ScholarCross Ref
- Bechhofer, R. E., and D. M. Goldsman. 1986. Truncation of the Bechhofer-Kiefer-Sobel procedure for selecting the multinomial event which has the largest probability (II): extended tables and an improved procedure. Communications in Statistics-Simulation and Computation B15:829- 851.Google ScholarCross Ref
- Bechhofer, R. E., D. M. Goldsman and T. J. Santner. 1995. Design and analysis for statistical selection, screening and multiple comparisons. New York: John Wiley & Sons, Inc.Google Scholar
- Bechhofer, R. E., ana B. W. Turnbull. 1978. Two (k+l)-decision selection procedures for comparing k normal means with a control. Journal of the American Statistical Association 73:385-392.Google Scholar
- Chen, H. J., and H. M. Zhang. 1993. Procedures for simultaneous estimation and selection of the best population with a preliminary test. American Journal of Mathematics and Management Science 13:195-216.Google Scholar
- Damerdji, H., P. W. Glynn, M. K. Nakayama and J. R. Wilson. 1996. Selecting the best system in transient simulations with variances known. Proceedings of the 1996 Winter Simulation Conference, ed. J. M. Charnes, D. M. Morrice, D. T. Brunner and J. J. Swain! 281-286. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Dudewisz, E. J., and S. R. Dalal. 1975. Allocation of observations in ranking and selection with unequal variances. SankyE B37:28-78.Google Scholar
- Gibbons, J. D., I. Olkin and M. Sobel. 1977. Selecting and ordering populations: A new statistical methodology. New York: John Wiley & Sons, Inc. Google ScholarDigital Library
- Goldsman, D., and B. L. Nelson. 1994. Ranking, selection and multiple comparisons in computer simulation. In Proceedings of the 1994 Winter Simulation Conference, ed. J. D. Tew, M. S. Mannivannan, D. A. Sadowski, and A. F. Seila, 192- 199. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Goldsman, D., B. L. Nelson and B. Schmeiser. 1991. Methods for selecting the best system. In Proceedings of the 1991 Winter Simulation Conference, ed. B. L. Nelson, W. D. Kelton, and G. M. Clark, 177-186. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Gupta, S. S. 1965. On some multiple decision (selection and ranking) rules. Technometrics 7:225- 245.Google ScholarCross Ref
- Gupta, S. S., and D.-Y. Huang. 1976. Subset selection procedures for the means and variances of normal populations: unequal sample sizes. SankyTi B38:112-128.Google Scholar
- Gupta, S. S., and S. Panchepakesan. 1979. Multiple decision procedures. New York: John Wiley & Sons, Inc.Google Scholar
- Gupta, S. S., and M. Sobel. 1958. On selecting a subset which contains all populations better than a standard. Annals of Mathematical Statistics 28:957-967.Google ScholarCross Ref
- Gupta, S. S., and M. Sobel. 1960. Selecting a subset containing the best of several binomial populations. Chapter 20 in Contributions to Probability and Statistics, ed. Ulkin, Ghurye, Hoeffding, Madow and Mann. Stanford, California: Stanford University Press.Google Scholar
- Hochberg, Y., and A. C. Tamhane. 1987. Multiple comparison procedures. New York: John Wiley & Sons, Inc. Google ScholarDigital Library
- Hsu, J. C. 1984. Constrained simultaneous confidence intervals for multiple comparisons with the best. Annals of Statistics 12:1136-1144.Google ScholarCross Ref
- Hsu, J. C. 1996. Multiple comparisons: Theory and methods. London, England: Chapman and Hall.Google Scholar
- Hsu, J. C., and M. Peruggia. 1994. Graphical representation of Tukey's multiple comparison method. Journal of Computational and Graphical Statistics 3:143-161.Google Scholar
- Kannan, P. K., and S. M. Sanchez. 1994. Competitive market structures: A subset selection analysis. Management Science 40(11):1484-1499. Google ScholarDigital Library
- Kelton, W. D. 1997. Statistical analysis of simulation output. Proceedings of the 1997 Winter Simulation Conference, ed. S. Andradbttir, K. J. Healy, D. H. Withers, and B. L. Nelson. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Law, A. M., and W. D. Kelton. 1991. Simulation modeling and analysis, 2nd ed. New York: McGraw-Hill. Google ScholarDigital Library
- Matejcik, F. J., and B. L. Nelson. 1993. Simultaneous ranking, selection and multiple comparisons for simuiation. Proceedings of the iW -Winter Simulation Conference, ed. G. W. Evans, M. Mollaghasemi, E. C. Russell and W. E. Biles, 386-692. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Matejcik, F. J., and B. L. Nelson. 1995. Two-stage multiple comparisons with the best for computer simulation. Operations Research 43(4):633-640.Google ScholarDigital Library
- Miller, J. O., B. L. Nelson and C. H. Reilly. 1996. 2nd ed. New York: Springer-Verlag.Google Scholar
- Miller, J. O., B. L. Nelson and C. H. Reilly. 1996. Getting more from the data in a multinomial selection problem. Proceedings of the 1996 Winter Simulation Conference, ed. J. M. Charnes, D. M. Morrice, D. T. Brunner and J. J. Swain, 287-294. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Mukhodpadhyay, N., and T. S. Solanky. 1994. Multistage selection and ranking procedures. New York: Marcel Dekker.Google Scholar
- Nakayama, M. 1995. Selecting the best system in steady-state simulations using batch means. In Proceedings of the 1995 Winter Simulation Conference, ed. C. Alexopoulus, K. Kang, W. R. Lilegdon and D. Goldsman, 362-366. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Nelson, B. L. 1993. Robust multiple comparisons under common random numbers. Special issue on variance reduction techniques of ACM Tmnsactions on Modeling and Computer Simulation 3:225-243. Google ScholarDigital Library
- Nelson, B. L., and J. C. Hsu. 1993. Control-variate models of common random numbers for multiple comparisons with the best. Management Science 39(8):989-1001. Google ScholarDigital Library
- Nelson, B. L., and F. J. Matejcik. 1995. Using common random numbers for indifference-zone selection and multiple comparisons. Management Science 41(12):1935-1945. Google ScholarDigital Library
- Nelson, B. L., and M. Yuan, 1993. Multipie comparisons with the best for steady-state simulation. Technical note in ACM Transactions on Modeling and Computer Simulation 3:66-79. Google ScholarDigital Library
- Paulson, E. 1952. On the comparison of several experimental categories with a control. Annals of Mathematical Statistics 23:239-246.Google ScholarCross Ref
- Ramberg, J. S., S. M. Sanchez, P. J. Sanchez, and L. J. Hollick. 1991. Designing simulation experiments: Taguchi methods and response surface metamodels. Proceedings of the 1991 Winter Simulation Conference, ed. B. L. Nelson, W. D. Kelton, and G. M. Clark, 167-176. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Rinott, Y. 1978. On two-stage seiection procedures and related probability inequalities. Communications in Statistics: Theory and Methods A7(8):799-811.Google ScholarCross Ref
- Sanchez, S. M. 1987a. A modified least-failures sampling procedure for Bernoulli subset selection. Communications in Statistics-Theory and Methods 16(12):3609-3629.Google ScholarCross Ref
- Sanchez, S. M. 1987b. Small-sample performance of a modified least-failures sampling procedure for Bernoulli subset selection, Communications in Statistics-Simulation and Computation 16(4):1051-1065.Google ScholarCross Ref
- Sanchez, S. M. 1994. A robust design tutorial. Proceedings of the 1994 Winter Simulation Conference, eds. J. D. Tew, M. S. Manivannan, D. A. Sadowski, and A. F. Seila, 106-113. Institute of Electrical and Electronics Engineers, Piscataway, New Jersey. Google ScholarDigital Library
- Sanchez, S. M., and J. L. Higle, 1922; Observational studies of rare events: a subset selection approach. Journal of the American Statistical Association 87(149):878-883.Google Scholar
- Sanchez, S. M., P. J. Sanchez, J. S. Ramberg and F. Moeeni. 1996. Effective engineering design through simulation. International Transactions on Operational Research 3(2):169-185.Google ScholarCross Ref
- Taguchi, G. 1986. Introduction to quality engineering. White Plains, New York: UNIPUB/Krauss International.Google Scholar
- Tamhane, A. C. 1977. Multiple comparisons in model I: One-way ANOVA with unequal variances. Communications in Statistics-Theory and Methods A6:15-32.Google ScholarCross Ref
- Thesen, A., and L. E. Travis. 1992. Simulation for decision making. St. Paul, Minnesota: West Publishing Company. Google ScholarDigital Library
- Yang, W.-N., and B. L. Nelson. 1991. Using common random numbers and control variates in multiple-comparison procedures. Operations Research 39(4):583-591.Google ScholarDigital Library
Index Terms
- It is a far, far better mean I find…
Recommendations
Going far, logically
IJCAI'05: Proceedings of the 19th international joint conference on Artificial intelligenceThere are numerous applications where we need to ensure that multiple moving objects are sufficiently far apart. Furthermore, in many moving object domains, there is positional indeterminacy--we are not 100% sure exactly when a given moving object will ...
Comparison of Near and Far Infrared Vision for Biometric Applications
CCATS '15: Proceedings of the 2015 International Conference on Computer Application TechnologiesIn this paper, we provide the idea of analyzing the infrared vein patterns in the back of the hand combined with the appendant optical picture of the back of the hand for biometric purposes. We present results of an experiment which will demonstrate ...
Designing Far Memory Data Structures: Think Outside the Box
HotOS '19: Proceedings of the Workshop on Hot Topics in Operating SystemsTechnologies like RDMA and Gen-Z, which give access to memory outside the box, are gaining in popularity. These technologies provide the abstraction of far memory, where memory is attached to the network and can be accessed by remote processors without ...
Comments