Bibliografía

Bassham, L., Rukhin, A., Soto, J., Nechvatal, J., Smid, M., Leigh, S., Levenson, M., Vangel, M., Heckert, N., y Banks, D. (2010). A Statistical Test Suite for Random and Pseudorandom Number Generators for Cryptographic Applications. Special Publication (NIST SP), National Institute of Standards; Technology, Gaithersburg, MD. https://doi.org/10.6028/NIST.SP.800-22r1a

Bratley, P., Fox, B. L., y Schrage, L. E. (1983). A Guide to Simulation. Springer. https://link.springer.com/book/10.1007/978-1-4684-0167-7

Cao Abad, R. (2002). Introducción a la Simulación y a la Teoría de Colas. Netbiblo. https://books.google.es/books?id=lET6IPBm2vMC

Chen, H.-C., y Asau, Y. (1974). On generating random variates from an empirical distribution. AIIE Transactions, 6(2), 163-166.

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Davies, R. B., y Harte, D. S. (1987). Tests for Hurst effect. Biometrika, 74(1), 95-101.

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

Devroye, L. (1986). Non-Uniform Random Variate Generation. Springer. https://link.springer.com/book/10.1007/978-1-4613-8643-8

Dietrich, C. R., y Newsam, G. N. (1997). Fast and exact simulation of stationary Gaussian processes through circulant embedding of the covariance matrix. SIAM Journal on Scientific Computing, 18(4), 1088-1107.

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

Fernández-Casal, R., Costa, J., y Oviedo, M. (2024). Métodos predictivos de aprendizaje estadístico. Servizo de Publicacións. Universidade da Coruña. https://doi.org/10.17979/spudc.9788497498937

Fox, B. L. (1963). Generation of random samples from the beta F distributions. Technometrics, 5(2), 269-270.

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

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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

Hull, T. E., y Dobell, A. R. (1962). Random number generators. SIAM review, 4(3), 230-254.

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

Jöhnk, M. (1964). Generation of beta distribution and gamma distribution random variates. Metrika, 8, 5-15.

Kinderman, A. J., y Monahan, J. F. (1977). Computer Generation of Random Variables Using the Ratio of Uniform Deviates. ACM Transactions on Mathematical Software, 3(3), 257-260. https://doi.org/10.1145/355744.355750

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.

Kronmal, R. A., y Peterson Jr, A. V. (1979). On the alias method for generating random variables from a discrete distribution. The American Statistician, 33(4), 214-218.

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

Lehmer, D. H. (1951). Mathematical models in large-scale computing units. The Annals of the Computation Laboratory of Harvard University, 26, 141-146.

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

Marsaglia, G. (1968). Random numbers fall mainly in the planes. Proceedings of the National Academy of Sciences, 61(1), 25-28.

Marsaglia, G. (1977). The squeeze method for generating gamma variates. Computers & Mathematics with Applications, 3(4), 321-325.

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

Metropolis, N., y Ulam, S. (1949). The monte carlo method. Journal of the American Statistical Association, 44(247), 335-341.

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

Ross, S. M. (1999). Simulation. Prentice Hall.

Rotenberg, A. (1960). A new pseudo-random number generator. Journal of the ACM, 7(1), 75-77.

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.

Schrage, L. (1979). A more portable Fortran random number generator. ACM Transactions on Mathematical Software (TOMS), 5(2), 132-138.

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

Spinoza, B. (1677). Ethics.

Tausworthe, R. C. (1965). Random numbers generated by linear recurrence modulo two. Mathematics of Computation, 19(90), 201-209.

Von Neumann, J. (1951). Various techniques used in connection with random digits. Journal of Research of the National Institute of Standards, Appl. Math. Ser., 3, 36-38.

Walker, A. J. (1977). An efficient method for generating discrete random variables with general distributions. ACM Transactions on Mathematical Software (TOMS), 3(3), 253-256.

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