Testing Hypotheses on Simulated Data: Why Traditional Hypotheses-Testing Statistics Are Not Always Adequate for Simulated Data, and How to Modify Them
Richard Aló*, Vladik Kreinovich**, and Scott A. Starks**
*Center for Computational Sciences and Advanced Distributed Simulation, University of Houston-Downtown, One Main Street, Houston, TX 77002, USA
**Pan-American Center for Earth and Environmental Studies, University of Texas at El Paso, El Paso, TX 79968, USA
-  P. R. Cohen, “Empirical Methods for Artificial Intelligence,” MIT Press, Cambridge, Massachusetts, 1995.
-  P. R. Cohen, I. Gent, and T. Walsh, “Empirical Methods for Artificial Intelligence and Computer Science,” Tutorial at the 17th National Conference on Artificial Intelligence AAAI’2000, Austin, TX, July 30-August 3, 2000.
-  I. Gent, and T. Walsh, “An Empirical Analysis of Search in GSAT,” Journal of Artificial Intelligence Research, Vol.1, pp. 47-59, 1993.
-  C. McGeoch, P. Sanders, R. Fleischer, P. R. Cohen, and D. Precup, “Using Finite Experiments to Study Asymptotic Performance,” In: R. Fleischer, B. Moret, and M. Schmidt (eds.), Experimental Algorithmics, Springer-Verlag, Berlin, Heidelberg, New York, pp. 93-124, 2002.
-  D. J. Sheskin, “Handbook of Parametric and Nonparametric Statistical Procedures,” Chapman & Hall/CRC, Boca Raton, Florida, 2004.
-  H. M. Wadsworth Jr., “Handbook of statistical methods for engineers and scientists,” McGraw-Hill, N.Y., 1990.
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