Bibliografía

Demirhan, H., y Bitirim, N. (2016). CryptRndTest: An R Package for Testing the Cryptographic Randomness. The R Journal, 8(1), 233-247. https://doi.org/10.32614/rj-2016-016

Downham, D. Y. (1970). Algorithm AS 29: The Runs Up and Down Test. Applied Statistics, 19(2), 190-192. https://doi.org/10.2307/2346558

Gilks, W. R., y Wild, P. (1992). Adaptive Rejection Sampling for Gibbs Sampling. Applied Statistics, 41(2), 337-348. https://doi.org/10.2307/2347565

Hall, S. W. (1994). Analysis of defectivity of semiconductor wafers by contingency table. Proceedings of the Institute of Environmental Sciences, 1, 177-183.

Hofert, M., Kojadinovic, I., Mächler, M., y Yan, J. (2018). Elements of Copula Modeling with R. Springer. https://doi.org/10.1007/978-3-319-89635-9

Hörmann, W. (1995). A rejection technique for sampling from T-concave distributions. ACM Transactions on Mathematical Software (TOMS), 21(2), 182-193.

Hörmann, W., Leydold, J., y Derflinger, G. (2004). Automatic Nonuniform Random Variate Generation. Springer. https://link.springer.com/book/10.1007/978-3-662-05946-3

Hyndman, R. J., y Athanasopoulos, G. (2018). Forecasting: principles and practice (2nd edition). OTexts. https://otexts.com/fpp2

Knuth, D. E. (1969). The Art of Computer Programming: Semi-numerical algorithms (Vol. 2). Addison-Wesley. https://www-cs-faculty.stanford.edu/~knuth/taocp.html

Knuth, D. E. (2002). The Art of Computer Programming (tercera, Vol. 2). Addison-Wesley.

L’Ecuyer, P. (1999). Good Parameters and Implementations for Combined Multiple Recursive Random Number Generators. Operations Research, 47(1), 159-164. https://doi.org/10.1287/opre.47.1.159

L’Ecuyer, P., y Simard, R. (2007). TestU01. ACM Transactions on Mathematical Software, 33(4), 1-40. https://doi.org/10.1145/1268776.1268777

Liu, J. S. (2004). Monte Carlo Strategies in Scientific Computing. Springer. https://doi.org/10.1007/978-0-387-76371-2

Marsaglia, G., y Tsang, W. W. (2002). Some Difficult-to-Pass Tests of Randomness. Journal of Statistical Software, 7(3). https://doi.org/10.18637/jss.v007.i03

Marsaglia, G., Zaman, A., y Tsang, W. W. (1990). Toward a universal random number generator. Statistics & Probability Letters, 9(1), 35-39. https://doi.org/10.1016/0167-7152(90)90092-l

Matsumoto, M., y Nishimura, T. (1998). Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Transactions on Modeling and Computer Simulation, 8(1), 3-30. https://doi.org/10.1145/272991.272995

Nelsen, R. B. (2006). An introduction to copulas (2nd ed.). Springer.

Odeh, R. E., y Evans, J. O. (1974). Algorithm AS 70: The Percentage Points of the Normal Distribution. Applied Statistics, 23(1), 96-97. https://doi.org/10.2307/2347061

Park, S. K., y Miller, K. W. (1988). Random number generators: good ones are hard to find. Communications of the ACM, 31(10), 1192-1201. https://doi.org/10.1145/63039.63042

Park, S. K., Miller, K. W., y Stockmeyer, P. K. (1993). Technical correspondence: Response. Communications of the ACM, 36(7), 108-110.

Patefield, W. M. (1981). Algorithm AS 159: An Efficient Method of Generating Random R \(\times\) C Tables with Given Row and Column Totals. Applied Statistics, 30(1), 91-97. https://doi.org/10.2307/2346669

Ripley, B. D. (1987). Stochastic Simulation. Wiley. https://www.wiley.com/en-us/Stochastic+Simulation-p-9780470009604

Rubin, D. B. (1987). The calculation of posterior distributions by data augmentation: Comment: A noniterative sampling/importance resampling alternative to the data augmentation algorithm for creating a few imputations when fractions of missing information are modest: The SIR algorithm. Journal of the American Statistical Association, 82(398), 543-546.

Shannon, R. E. (1975). Systems Simulation: The Art and Science. Prentice-Hall.

Spinoza, B. (1677). Ethics.

Wichura, M. J. (1988). Algorithm AS 241: The Percentage Points of the Normal Distribution. Applied Statistics, 37(3), 477-484. https://doi.org/10.2307/2347330