References

[1] M. Abramowitz and I. A. Stegun, editors. Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables. Dover, New York, 1972.

[2] D. Adler and D. Murdoch. rgl: 3D visualization device system(OpenGL), 2007. R package version 0.74.

[3] J. H. Ahrens and U. Dieter. Computer methods for sampling from the exponential and normal distributions. Comm. ACM, 15:873–882, 1972.

[4] J. H. Ahrens and U. Dieter. Sampling from the binomial and Poisson distributions: A method with bounded computation times. Computing, 25:193–208, 1980.

[5] J. Albert. Bayesian Computation with R. Springer, New York, 2007.

[6] J. H. Albert. Teaching Bayesian statistics using sampling methods and MINITAB. The American Statistician, 47:182–191, 1993.

[7] P. M. E. Altham. Exact Bayesian analysis of a 2 × 2 contingency table and Fisher’s “exact” significance test. Journal of the Royal Statistical Society. Series B, 31:261–269, 1969.

[8] T. W. Anderson. An Introduction to Multivariate Statistical Analysis. Wiley, New York, Second edition, 1984.

[9] T. W. Anderson and D. A. Darling. A test of goodness-of-fit. Journal of the American Statistical Association, 49:765–769, 1954.

[10] D. F. Andrews. Plots of high dimensional data. Biometrics, 28:125–136, 1972.

[11] F. J. Anscombe and W. J. Glynn. Distribution of the kurtosis statistic b2 for normal statistics. Biometrika, 70:227–234, 1986.

[12] S. Arya and D. M. Mount. Approximate nearest neighbor searching. In Proceedings of the fourth annual ACM-SIAM Symposium on Discrete Algorithms (SODA 1993), pages 271–280, 1993.

[13] S. Arya, D. M. Mount, N. S. Netanyahu, R. Silverman, and A. Y. Wu. An optimal algorithm for approximate nearest neighbor searching. Journal of the ACM, 45:891–923, 1998.

[14] D. Asimov. The grand tour: a tool for viewing multidimensional data. SIAM Journal on Scientific and Statistical Computing, 6(1):128–143, 1985.

[15] A. Azzalini and A. W. Bowman. A look at some data on the Old Faithful geyser. Applied Statistics, 39:357–365, 1990.

[16] L. J. Bain and M. Engelhardt. Introduction to Probability and Mathematical Statistics. Duxbury Classic Series. Brooks-Cole, Pacific Grove, CA, 1991.

[17] N. K. Bakirov, M. L. Rizzo, and G. J. Székely. A multivariate nonparametric test of independence. Journal of Multivariate Analysis, 93:1742– 1756.

[18] J. Banks, J. Carson, B. L. Nelson, and D. Nicol. Discrete-Event System Simulation. Prentice-Hall, Upper Saddle River, NJ, fourth edition, 2004.

[19] P. Barbe and P. Bertail. The Weighted Bootstrap. Springer, New York, 1995.

[20] L. Baringhaus and C. Franz. On a new multivariate two-sample test. Journal of Multivariate Analysis, 88:190–206, 2004.

[21] M. S. Bartlett. On the theory of statistical regression. Proceedings of the Royal Society of Edinburgh, 53:260–283.

[22] K. E. Basford and G. J. McLachlan. Likelihood estimation with normal mixture models. Journal of the Royal Statistical Society. Series C, 34(3):282–289, 1985.

[23] M. A. Bean. Probability: The Science of Uncertainty with Applications to Investments, Insurance, and Engineering. Brooks-Cole, Pacific Grove, CA, 2001.

[24] R. A. Becker, J. M. Chambers, and A. R. Wilks. The New S Language: A Programming Environment for Data Analysis and Graphics. Wadsworth & Brooks/Cole, Pacific Grove, CA, 1988.

[25] J. L. Bentley. Multidimensional binary search trees used for associative searching. Communications of the ACM, 18(9):509–517, 1975.

[26] M. Berkelaar et al. lpSolve: Interface to Lp solve v. 5.5 to solve linear/integer programs, 2006. R package version 5.5.7.

[27] P. J. Bickel. A distribution free version of the Smirnov two-sample test in the multivariate case. Annals of Mathematical Statistics, 40:1–23.

[28] P. J. Bickel and L. Breiman. Sums of functions of nearest neighbor distances, moment bounds, limit theorems and a goodness of fit test. Annals of Probability, 11:185–214, 1983.

[29] A. W. Bowman and A. Azzalini. sm: Smoothing methods for nonparametric regression and density estimation, 2005. Ported to R by B. D. Ripley up to version 2.0 and later versions by Adrian W. Bowman and Adelchi Azzalini. R package version 2.1-0.

[30] A. W. Bowman, P. Hall, and D. M. Titterington. Cross-validation in nonparametric estimation of probabilities and probability densities. Biometrika, 71(2):341–351, 1984.

[31] G. E. P. Box and M. E. Müller. A note on the generation of random normal deviates. The Annals of Mathematical Statistics, 29:610–611, 1958.

[32] R. Brent. Algorithms for Minimization without Derivatives. Prentice-Hall, New Jersey, 1973.

[33] S. P. Brooks, P. Dellaportas, and G. O. Roberts. An approach to diagnosing total variation convergence of MCMC algorithms. Journal of Computational and Graphical Statistics, 6(3):251–265, 1997.

[34] A. Canty and B. Ripley. boot: Bootstrap R (S-Plus) Functions (Canty), 2006. S original by Angelo Canty, R port by Brian Ripley. R package version 1.2-28.

[35] O. Cappe and C. P. Robert. Markov Chain Monte Carlo: 10 years and still running! Journal of the American Statistical Association, 95(452):1282–1286, 2000.

[36] A. E. Carlin, B. P. Gelfand and A. F. M. Smith. Hierarchical Bayesian analysis of changepoint problems. Applied Statistics, 41:389–405, 1992.

[37] B. P. Carlin and T. A. Louis. Bayes and Empirical Bayes Methods for Data Analysis. Chapman and Hall/CRC, Boca Raton, FL, 2000.

[38] D. Carr. hexbin: Hexagonal Binning Routines, 2006. Ported by Nicholas Lewin-Koh and Martin Maechler. R package version 1.8.0.

[39] G. Casella and R. Berger. Statistical Inference. Duxbury Press, Belmont, California, 1990.

[40] G. Casella and E. E. George. Explaining the Gibbs sampler. The American Statistician, 46:167–174, 1992.

[41] J. M. Chambers. Programming with Data: A Guide to the S Language. Springer, New York, 1998.

[42] J. M. Chambers and T. J. Hastie. Statistical Models in S. Chapman & Hall, London, 1992.

[43] J. M. Chambers, C. L. Mallows, and B. W. Stuck. A method for simulating stable random variables. Journal of the American Statistical Association, 71:304–344, 1976.

[44] M.-H. Chen, Q.-M. Shao, and J. G. Ibrahim. Monte Carlo Methods in Bayesian Computation. Springer, New York, 2000.

[45] M. A. Chernick. Bootstrap Methods: A Practitioner’s Guide. Wiley, New York, 1999.

[46] H. Chernoff. The use of faces to represent points in k-dimensional space graphically. Journal of the American Statistical Association, 68:361– 368, 1973.

[47] S. Chib and E. Greenberg. Understanding the Metropolis-Hastings algorithm. The American Statistician, 49:327–335, 1995.

[48] W. S. Cleveland. Visualizing Data. Summit Press, New Jersey, 1993.

[49] W. S. Cleveland. Coplots, nonparametric regression, and conditionally parametric fits. In Multivariate Analysis and its Applications (Hong Kong, 1992), volume 24 of IMS Lecture Notes Monograph Series, pages 21–36. Inst. Math. Statist., Hayward, CA, 1994.

[50] W. S. Cleveland and R. McGill. The many faces of a scatterplot. Journal of the American Statistical Association, 79(388):807–822, 1984.

[51] J.-F. Coeurjolly. Simulation and identification of the fractional Brownian motion: a bibliographical and comparative study. Journal of Statistical Software, 5, 2000.

[52] D. Cook and D. F. Swayne. Interactive and Dynamic Graphics for Data Analysis: With R and GGobi. Springer, New York, 2007.

[53] G. Cornuejols and R. Tütüncü. Optimization Methods in Finance. Cambridge University Press, Cambridge, 2007.

[54] M. K. Cowles and B. P. Carlin. Markov Chain Monte Carlo convergence diagnostics: A comparative review. Journal of the American Statistical Association, 91(434):883–904, 1996.

[55] D. R. Cox and N. J. H. Small. Testing multivariate normality. Biometrika, 65:263–272, 1978.

[56] H. Cramér. On the composition of elementary errors. II Statistical applications. Skandinavisk Aktuarietidskrift, 11:141–180, 1928.

[57] M. J. Crawley. Statistical Computing: An Introduction to Data Analysis using S-Plus. Wiley, New York, 2002.

[58] R. B. D’Agostino. Tests for the normal distribution. In R. B. D’Agostino and M. A. Stephens, editors, Goodness-of-Fit Techniques, pages 367– 420. Marcel Dekker, New York, 1986.

[59] R. B. D’Agostino and E. S. Pearson. Tests for departure from normality. empirical results for the distributions of b2 and b1. Biometrika, 60:613–622, 1973.

[60] R. B. D’Agostino and M. A. Stephens. Goodness-of-Fit Techniques. Marcel Dekker, New York, 1986.

[61] D. B. Dahl. xtable: Export tables to LaTeX or HTML, 2007. With contributions from many others. R package version 1.4-3.

[62] P. Dalgaard. Introductory Statistics with R. Springer, New York, 2002.

[63] A. C. Davison and D. V. Hinkley. Bootstrap Methods and their Application. Cambridge University Press, Oxford, 1997.

[64] M. H. DeGroot and M. J. Schervish. Probability and Statistics. Addison-Wesley, New York, third edition, 2002.

[65] S. Déjean and S. Cohen. FracSim: An R package to simulate multifractional Lévy motions. Journal of Statistical Software, 14, 2005.

[66] S. Déjean and S. Cohen. FracSim: Simulation of Lévy motions, 2005. R package version 0.2.

[67] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society. Series B. Methodological, 39:1–38, 1977.

[68] L. Devroye. The computer generation of Poisson random variables. Computing, 26:197–207, 1981.

[69] L. Devroye. Non-Uniform Random Variate Generation. Springer, New York, 1986.

[70] L. Devroye. A Course in Density Estimation. Birkhäuser, Boston, 1987.

[71] L. Devroye and L. Györfi. Nonparametric Density Estimation: The L1 View. John Wiley, New York, 1985.

[72] E. Dimitriadou, K. Hornik, F. Leisch, D. Meyer, and A. Weingessel. e1071: Misc Functions of the Department of Statistics (e1071), TU Wien, 2006. R package version 1.5-16.

[73] D. P. Doane. Aesthetic frequency classification. The American Statistician, 30:181–183, 1976.

[74] M. Dresher. Games of Strategy: Theory and Application. Dover, New York, 1981.

[75] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. Wiley, New York, second edition, 2001.

[76] T. Duong. ks: Kernel smoothing, 2007. R package version 1.4.9.

[77] R. Durrett. Probability: Theory and Examples. Wadsworth Publishing (Duxbury Press), Belmont, CA, second edition, 1996.

[78] R. Eckhardt. Stan Ulam, John von Neumann, and the Monte Carlo method. Los Alamos Science, (15, Special Issue):131–137, 1987. With contributions by Tony Warnock, Gary D. Doolen, and John Hendricks, Stanislaw Ulam 1909–1984.

[79] S. Efromovich. Density estimation for the case of supersmooth measurement error. Journal of the American Statistical Association, 92(438):526–535, 1997.

[80] B. Efron. Bootstrap methods: another look at the jackknife. Annals of Statistics, 7:1–26, 1979.

[81] B. Efron. Nonparametric estimates of standard error: the jackknife, the bootstrap, and other methods. Biometrika, 68:589–599, 1981.

[82] B. Efron. Nonparametric standard errors and confidence intervals (with discussion). Canadian Journal of Statistics, 9:139–172, 1981.

[83] B. Efron. The Jackknife, the Bootstrap and Other Resampling Plans. Society for Industrial and Applied Mathematics, Philadelphia, 1982.

[84] B. Efron and R. J. Tibshirani. An Introduction to the Bootstrap. Chapman & Hall/CRC, Boca Raton, FL, 1993.

[85] V. K. Epanechnikov. Non-parametric estimation of a multivariate probability density. Theory of Probability and its Applications, 14:153–158, 1969.

[86] R. L. Eubank. Spline Smoothing and Nonparametric Regression. Marcel Dekker, New York, 1988.

[87] M. Evans and T. Schwartz. Approximating Integrals via Monte Carlo and Deterministic Methods. Oxford University Press, Oxford, 2000.

[88] B. Everitt and T. Hothorn. A Handbook of Statistical Analyses Using R. Chapman & Hall/CRC, Boca Raton, FL, 2006.

[89] B. S. Everitt and D. J. Hand. Finite Mixture Distributions. Chapman & Hall, London, 1981.

[90] J. J. Faraway. Linear Models with R. Chapman & Hall/CRC, Boca Raton, FL, 2004.

[91] J. J. Faraway. Extending Linear Models with R: Generalized Linear, Mixed Effects and Nonparametric Regression Models. Chapman & Hall/CRC, Boca Raton, FL, 2006.

[92] R. A. Fisher. On the ‘probable error’ of a coefficient of correlation deduced from a small sample. Metron, 1:3–32, 1921.

[93] R. A. Fisher. The moments of the distribution for normal samples of measures of departures from normality. Proceedings of the Royal Society of London, A, 130:16–28, 1930.

[94] G. S. Fishman. Monte Carlo Concepts, Algorithms, and Applications. Springer, New York, 1995.

[95] G. S. Fishman. Discrete-Event Simulation. Springer, New York, 2001.

[96] R. Fletcher and C. M. Reeves. Function minimization by conjugate gradients. Computer Journal, 7:148–154.

[97] J. Fox. An R and S-Plus Companion to Applied Regression. Sage Publications, Thousand Oaks, CA, 2002.

[98] J. N. Franklin. Numerical simulation of stationary and non-stationary Gaussian random processes. SIAM Review, 7:68–80.

[99] D. Freedman and P. Diaconis. On the histogram as a density estimator: L2 theory. Zeitschrift für Wahrscheinlichkeitstheorie und verwandte Gebiete, 57:453–476.

[100] J. Friedman and J. Tukey. A projection pursuit algorithm for exploratory data analysis. IEEE Transactions on Computers, 23:881–889, 1975.

[101] J. H. Friedman and L. C. Rafsky. Multivariate generalizations of the Wald-Wolfowitz and Smirnov two-sample tests. Annals of Statistics, 7:697–717, 1979.

[102] M. Friendly. Visualizing Categorical Data. SAS Press, Cary, NC, 2000.

[103] D. Gamerman. Markov Chain Monte Carlo. Stochastic simulation for Bayesian inference. Chapman & Hall, London, 1997.

[104] A. E. Gelfand. Gibbs sampling. Journal of the American Statistical Association, 95(452):1300–1304, 2000.

[105] A. E. Gelfand, S. E. Hills, A. Racine-Poon, and A. F. M. Smith. Illustration of Bayesian inference in normal data models using Gibbs sampling. Journal of the American Statistical Association, 85:972–985, 1990.

[106] A. E. Gelfand and A. F. M. Smith. Sampling based approaches to calculating marginal densities. Journal of the American Statistical Association, 85:398–409, 1990.

[107] A. Gelman. Inference and monitoring convergence. In W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, editors, Markov Chain Monte Carlo in Practice, pages 131–143. Chapman & Hall, Boca Raton, FL, 1996.

[108] A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin. Bayesian Data Analysis. Chapman and Hall, Boca Raton, second edition, 2004.

[109] A. Gelman and D. B. Rubin. Inference from iterative simulation using multiple sequences (with discussion). Statistical Science, 7:457–511, 1992.

[110] A. Gelman and D. B. Rubin. A single sequence from the Gibbs sampler gives a false sense of security. In J. M. Bernardo, J. O. Berger, O. P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics 4, pages 625– 631. Oxford University Press, Oxford, 1992.

[111] S. Geman and D. Geman. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6:721–741, 1984.

[112] J. E. Gentle. Random Number Generation and Monte Carlo Methods. Springer, New York, 1998.

[113] J. E. Gentle. Elements of Computational Statistics. Springer, New York, 2002.

[114] J. E. Gentle, W. Härdle, and Y. Mori, editors. Handbook of Computational Statistics : Concepts and Methods. Springer, New York, 2004.

[115] A. Genz and F. Bretz. mvtnorm: Multivariate Normal and T Distribution, 2007. R port by Torsten Hothorn. R package version 0.8-1.

[116] C. J. Geyer. Practical Markov Chain Monte Carlo (with discussion). Statistical Science, 7:473–511, 1992.

[117] C. J. Geyer. mcmc: Markov Chain Monte Carlo, 2005. R package version 0.5-1.

[118] S. Ghahramani. Fundamentals of Probability. Prentice-Hall, New Jersey, second edition, 2000.

[119] W. R. Gilks. Full conditional distributions. In W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, editors, Markov Chain Monte Carlo in Practice, pages 75–88. Chapman & Hall, Boca Raton, FL, 1996.

[120] W. R. Gilks, S. Richardson, and D. J. Spiegelhalter. Markov Chain Monte Carlo in Practice. Chapman & Hall, Boca Raton, FL, 1996.

[121] G. H. Givens and J. A. Hoeting. Computational Statistics. Wiley, New Jersey, 2005.

[122] R. Gnanadesikan. Methods for the Statistical Analysis of Multivariate Observations. Wiley, New York, Second edition, 1997.

[123] C. Gu. gss: General Smoothing Splines. R package version 0.9-3.

[124] F. A. Haight. Handbook of the Poisson Distribution. Wiley, New York, 1967.

[125] P. Hall and J. S. Marron. Choice of kernel order in density estimation. Annals of Statistics, 16:161–173, 1987.

[126] D. J. Hand, F. Daly, A. D. Lunn, K. J. McConway, and E. Ostrokowski. A Handbook of Small Data Sets. Chapman & Hall, Boca Raton, FL, 1996.

[127] R. K. S. Hankin. gsl: wrapper for the Gnu Scientific Library, 2005. qrng functions by Duncan Murdoch. R package version 1.6-7.

[128] W. Härdle. Applied Nonparametric Regression, volume 19 of Econometric Society Monographs. Cambridge University Press, Cambridge, 1990.

[129] W. Härdle. Smoothing Techniques. Springer-Verlag, New York, 1991. With implementation in S.

[130] W. Härdle, M. Müller, S. Sperlich, and A. Werwatz. Nonparametric and Semiparametric Models. Springer-Verlag, New York, 2004.

[131] F. E. Harrell. Regression Modeling Strategies, with Applications to Linear Models, Survival Analysis and Logistic Regression. Springer, 2001.

[132] F. E. Harrell Jr. Hmisc: Harrell Miscellaneous, 2007. With contributions from many other users. R package version 3.3-1.

[133] H. O. Hartley and D. L. Harris. Monte Carlo computations in normal correlation problems. Journal of Association for Computing Machinery, 10:301–306, 1963.

[134] T. Hastie. gam: Generalized Additive Models, 2006. R package version 0.98.

[135] T. Hastie and R. Tibshirani. Generalized additive models. Statistical Science, 1(3):297–318, 1986. With discussion.

[136] T. Hastie, R. Tibshirani, and J. Friedman. The Elements of Statistical Learning. Springer Series in Statistics. Springer-Verlag, New York, 2001. Data mining, inference, and prediction.

[137] T. J. Hastie and R. J. Tibshirani. Generalized Additive Models. Chapman and Hall Ltd., London, 1990.

[138] W. K. Hastings. Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57:97–109, 1970.

[139] N. Henze. A multivariate two-sample test based on the number of nearest neighbor coincidences. Annals of Statistics, 16:772–783, 1988.

[140] N. Henze. On Mardia’s kurtosis test for multivariate normality. Communications in Statistics: Theory and Methods, 23:1031–1045, 1994.

[141] N. Henze. Extreme smoothing and testing for multivariate normality. Statistics and Probability Letters, 35:203–213, 1997.

[142] N. J. Higham. Accuracy and Stability of Numerical Algorithms. SIAM Publications, Philadelphia, 1996.

[143] J. S. U. Hjorth. Computer Intensive Statistical Methods: Validation, Model Selection and Bootstrap. Chapman and Hall, London, 1992.

[144] W. Hoeffding. A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics, 19:293–325, 1948.

[145] W. Hoeffding. A non-parametric test of independence. Annals of Mathematical Statistics, 19:546–547, 1948.

[146] R. V. Hogg, J. W. McKean, and A. T. Craig. Introduction to Mathematical Statistics. Prentice Hall, Upper Saddle River, New Jersey, sixth edition, 2005.

[147] K. Hornik. The R FAQ. R Foundation for Statistical Computing, Vienna, Austria, 2007. ISBN 3-900051-08-9.

[148] R. J. Hyndman and Y. Fan. Sample quantiles in statistical packages. The American Statistician, 50:361–365, 1996.

[149] S. M. Iacus. sde: Simulation and Inference for Stochastic Differential Equations, 2006. R package version 1.9.5.

[150] J. P. Imhof. Computing the distribution of quadratic forms in normal variables. Biometrika, 48(3/4):419–426, 1961.

[151] J. P. Imhof. Corrigenda: Computing the distribution of quadratic forms in normal variables. Biometrika, 49(1/2):284, 1962.

[152] A. Inselberg. The plane with parallel coordinates. The Visual Computer, 1:69–91, 1985.

[153] R. G. Jarrett. A note on the intervals between coal-mining disasters. Biometrika, 66:191–193, 1979.

[154] M. E. Johnson. Multivariate Statistical Simulation. Wiley, New York, 1987.

[155] M. E. Johnson, W. Chiang, and J. S. Ramberg. Generation of continuous multivariate distributions for statistical applications. American Journal of Mathematical Management Science, 4:225–248, 1984.

[156] N. L. Johnson, S. Kotz, and N. Balakrishnan. Continuous Univariate Distributions, volume 1. Wiley, New York, Second edition, 1994.

[157] N. L. Johnson, S. Kotz, and N. Balakrishnan. Continuous Univariate Distributions, volume 2. Wiley, New York, Second edition, 1995.

[158] N. L. Johnson, S. Kotz, and A. W. Kemp. Univariate Discrete Distributions. Wiley, New York, Second edition, 1992.

[159] A. W. Kemp. Efficient generation of logarithmically distributed pseudorandom variables. Applied Statistics, 30:249–253, 1981.

[160] S. E. Kemp. knnFinder: Fast Near Neighbour Search. R package version 1.0.

[161] W. J. Kennedy, Jr. and J. E. Gentle. Statistical Computing. Marcel Dekker, New York, 1980.

[162] D. A. King and J. H. Maindonald. Tree architecture in relation to leaf dimensions and tree stature in temperate and tropical rain forests. Journal of Ecology, 87:1012–1024, 1999.

[163] C. Kleiber and S. Kotz. Statistical Size Distributions in Economics and Actuarial Sciences. Wiley, 2003.

[164] D. B. Knuth. The Art of Computer Programming (Vol. 2: Seminumerical Algorithms). Addison-Wesley, Reading, third edition, 1997.

[165] D. Kundu and A. Basu, editors. Statistical Computing: Existing Methods and Recent Developments. Alpha Science International Ltd., Harrow, U.K., 2004.

[166] D. Kuonen. Saddlepoint approximations for distributions of quadratic forms in normal variables. Biometrika, 86(4):929–935, 1999.

[167] D. T. Lang, D. Swayne, H. Wickham, and M. Lawrence. rggobi: Interface between R and GGobi, 2006. R package version 2.1.4-4.

[168] K. Lange. Numerical Analysis for Statisticians. Springer-Verlag, New York, 1998.

[169] K. Lange. Optimization. Springer-Verlag, New York, 2004.

[170] R. J. Larsen and M. L. Marx. An Introduction to Mathematical Statistics and Its Applications. Prentice-Hall, Inc., New Jersey, fourth edition, 2006.

[171] P. M. Lee. Bayesian Statistics. Oxford University Press, New York, third edition, 2004.

[172] E. L. Lehmann. Testing Statistical Hypotheses. Springer, New York, second edition, 1986. Originally published New York: Wiley c1986.

[173] E. L. Lehmann and G. Casella. Theory of Point Estimation. Springer, New York, second edition, 1998.

[174] F. Leisch and E. Dimitriadou. mlbench: Machine Learning Benchmark Problems, 2007. R package version 1.1-3.

[175] R. V. Lenth. Algorithm AS 243 – Cumulative distribution function of the non-central t distribution. Applied Statistics, 38:185–189, 1989.

[176] A. Liaw and M. Wiener. Classification and regression by randomForest. R News, 2(3):18–22, 2002.

[177] U. Ligges. R-WinEdt. In K. Hornik, F. Leisch, and A. Zeileis, editors, Proceedings of the 3rd International Workshop on Distributed Statistical Computing (DSC 2003), TU Wien, Vienna, Austria, 2003. ISSN 1609-395X.

[178] R. J. A. Little and D. B. Rubin. Statistical Analysis with Missing Data. Wiley, Hoboken, NJ, second edition, 2002.

[179] J. S. Liu. Monte Carlo Strategies in Scientific Computing. Springer, New York, 2001.

[180] C. Loader. locfit: Local Regression, Likelihood and Density Estimation, 2006. R package version 1.5-3.

[181] C. R. Loader. Bandwidth selection: Classical or plug-in? The Annals of Statistics, 27:415–438, 1999.

[182] J. Ludbrook and H. Dudley. Why permutation tests are superior to t and F tests in biomedical research. The American Statistician, 52(2):127–132, 1998.

[183] M. Maechler and many others. sfsmisc: Utilities from Seminar fuer Statistik ETH Zurich, 2007. R package version 0.95-9.

[184] J. Maindonald and J. Braun. Data Analysis and Graphics Using R – an Example-based Approach. Cambridge University Press, Cambridge, 2003.

[185] J. Maindonald and J. Braun. DAAG: Data Analysis and Graphics, 2007. R package version 0.95.

[186] E. Mammen. When Does Bootstrap Work? Springer, New York, 1992.

[187] K. V. Mardia. Measures of multivariate skewness and kurtosis with applications. Biometrika, 57:519–530, 1970.

[188] K. V. Mardia, J. T. Kent, and J. M. Bibby. Multivariate Analysis. Academic Press, San Diego, 1979.

[189] J. S. Marron and M. P. Wand. Exact mean integrated squared error. The Annals of Statistics, 20:712–736, 1992.

[190] G. Marsaglia, W. W. Tsang, and J. Wang. Fast generation of discrete random variables. Journal of Statistical Software, 11, 2004.

[191] A. D. Martin and K. M. Quinn. MCMCpack: Markov Chain Monte Carlo (MCMC) Package, 2007. R package version 0.8-1.

[192] W. L. Martinez and A. R. Martinez. Computational Statistics Handbook with MATLAB. Chapman & Hall/CRC, Boca Raton, FL, 2002.

[193] R. N. McGrath and B. Y. Yeh. Count Five test for equal variance. The American Statistician, 59:47–53, 2005.

[194] G. J. McLachlan and T. Krishnan. The EM Algorithm and Extensions. Wiley, New York, 1997.

[195] N. Metropolis. The beginning of the Monte Carlo method. Los Alamos Science, (15, Special Issue):125–130, 1987. Stanislaw Ulam 1909–1984.

[196] N. Metropolis. The Los Alamos experience, 1943–1954. In A History of Scientific Computing (Princeton, NJ, 1987), ACM Press History Series, pages 237–250. ACM, New York, 1990.

[197] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller. Equations of state calculations by fast computing machine. Journal of Chemical Physics, 21:1087–1091, 1953.

[198] N. Metropolis and S. Ulam. The Monte Carlo method. Journal of the American Statistical Association, 44:335–341, 1949.

[199] D. Meyer, A. Zeileis, and K. Hornik. vcd: Visualizing Categorical Data, 2007. R package version 1.0.5.

[200] P. W. Mielke, Jr. and K. J. Berry. Permutation tests for common locations among samples with unequal variances. Journal of Educational and Behavioral Statistics, 19(3):217–236, 1994.

[201] I. Miller and M. Miller. John E. Freund’s Mathematical Statistics with Applications. Prentice Hall, New Jersey, seventh edition, 2004.

[202] J. F. Monahan. Numerical Methods of Statistics. Cambridge University Press, Cambridge, 2001.

[203] H. G. Müller. Nonparametric Regression Analysis of Longitudinal Data. Springer-Verlag, Berlin, 1988.

[204] P. Murrell. R Graphics. Chapman & Hall/CRC, Boca Raton, FL, 2005.

[205] J. A. Nelder and R. Mead. A simplex algorithm for function minimization. Computer Journal, 7:308, 1965.

[206] J. Nocedal and S. J. Wright. Numerical Optimization. Springer, New York, 1999.

[207] P. L. Odell and A. H. Feiveson. A numerical procedure to generate a sample covariance matrix. Journal of the American Statistical Association, 61:199–203.

[208] G. Owen. Game Theory. Academic Press, New York, third edition, 1995.

[209] W. M. Patefield. Algorithm AS159. An efficient method of generating r × c tables with given row and column totals. Applied Statistics, 30:91– 97, 1981.

[210] J. K. Patel and C. B. Read. Handbook of the Normal Distribution. Marcel Dekker, New York, second edition, 1996.

[211] J. C. Pinheiro and D. M. Bates. Mixed-Effects Models in S and S-Plus. Springer, 2000.

[212] M. Plummer, N. Best, K. Cowles, and K. Vines. coda: Output analysis and diagnostics for MCMC, 2007. R package version 0.11-2.

[213] W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, New York, Second edition, 1992.

[214] M. L. Puri and P. K. Sen. Nonparametric Methods in Multivariate Analysis. Wiley, New York, 1971.

[215] M. H. Quenouille. Approximate tests of correlation in time series. Journal of the Royal Statistical Society, Series B, 11:68–84, 1949.

[216] M. H. Quenouille. Notes on bias in estimation. Biometrika, 43(3/4):353– 360, 1956.

[217] R Development Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2007. ISBN 3-900051-07-0.

[218] R Development Core Team. R Installation and Administration. R Foundation for Statistical Computing, Vienna, Austria, 2007. ISBN 3-900051-09-07.

[219] A. E. Raftery and S. M. Lewis. How many iterations in the Gibbs sampler? In J. M. Bernardo, J. O. Berger, O. P. Dawid, and A. F. M. Smith, editors, Bayesian Statistics 4, pages 763–773. Oxford University Press, Oxford, 1992.

[220] C. R. Rao, editor. Linear Statistical Inference and Its Applications. Wiley, New York, second edition, 1973.

[221] C. R. Rao, editor. Computational Statistics. Elsevier, The Netherlands, 1993.

[222] C. R. Rao, E. J. Wegman, and J. L. Solka, editors. Handbook of Statistics, Volume 24: Data Mining and Data Visualization.

[223] B. D. Ripley. Stochastic Simulation. Cambridge University Press, Cambridge, 1987.

[224] B. D. Ripley. Pattern Recognition and Neural Networks. Cambridge University Press, Cambridge, 1996.

[225] B. D. Ripley and D. J. Murdoch. The R for Windows FAQ. R Foundation for Statistical Computing, Vienna, Austria, 2007.

[226] M. L. Rizzo and G. J. Székely. energy: E-statistics (energy statistics) tests of fit, independence, clustering, 2007. R package version 1.0-6.

[227] C. P. Robert. Convergence control methods for Markov Chain Monte Carlo algorithms. Statistical Science, 10(3):231–253, 1995.

[228] C. P. Robert and G. Casella. Monte Carlo Statistical Methods. Springer, New York, Second edition, 2004.

[229] G. O. Roberts. Markov chain concepts related to sampling algorithms. In W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, editors, Markov Chain Monte Carlo in Practice, pages 45–58. Chapman & Hall, Boca Raton, FL, 1996.

[230] G. O. Roberts, A. Gelman, and W. R. Gilks. Weak convergence and optimal scaling of random walk Metropolis algorithms. Annals of Applied Probability, 7:110–120, 1997.

[231] V. K. Rohatgi. An Introduction to Probability Theory and Mathematical Statistics. Wiley, New York, 1976.

[232] S. M. Ross. A First Course in Probability. Prentice-Hall, New Jersey, seventh edition, 2006.

[233] S. M. Ross. Simulation. Academic Press, San Diego, fourth edition, 2006.

[234] S. M. Ross. An Introduction to Probability Models. Academic Press, San Diego, ninth edition, 2007.

[235] J. P. Royston. Algorithm AS 181. The W test for normality. Applied Statistics, 31:176–180, 1982.

[236] J. P. Royston. An extension of Shapiro and Wilk’s W test for normality to large samples. Applied Statistics, 31(2):115–124, 1982.

[237] P. Royston. Approximating the Shapiro-Wilk W-test for non-normality. Statistical Computing, 2:117–119, 1992.

[238] R. Y. Rubinstein. Simulation and the Monte Carlo Method. Wiley, New York, 1981.

[239] D. Sarkar. lattice: Lattice Graphics, 2007. R package version 0.15-8.

[240] M. F. Schilling. Multivariate two-sample tests based on nearest neighbors. Journal of the American Statistical Association, 81:799–806, 1986.

[241] D. W. Scott. On optimal and data-based algorithms. Biometrika, 66:605–610, 1979.

[242] D. W. Scott. Averaged shifted histograms: Effective nonparametric density estimators in several dimensions. Annals of Statistics, 13:1024– 1040, 1985.

[243] D. W. Scott. Frequency polygons: Theory and application. Journal of the American Statistical Association, 80:348–354, 1985.

[244] D. W. Scott. Multivariate Density Estimation. Theory, Practice, and Visualization. John Wiley, New York, 1992.

[245] D. W. Scott and A. Gebhardt. ash: David Scott’s ASH routines. S original by David W. Scott, R port by Albrecht Gebhardt. R package version 1.0-9.

[246] P. K. Sen. On some multisample permutation tests based on a class of U-statistics. Journal of the American Statistical Association, 62(320):1201–1213, 1967.

[247] J. Shao and D. Tu. The Jackknife and Bootstrap. Springer-Verlag, New York, 1995.

[248] S. S. Shapiro and M. B. Wilk. An analysis of variance test for normality (complete samples). Biometrika, 52:591–611, 1965.

[249] S. S. Shapiro, M. B. Wilk, and H. J. Chen. A comparative study of various tests of normality. Journal of the American Statistical Association, 63:1343–1372, 1968.

[250] B. W. Silverman. Choosing the window width when estimating a density. Biometrika, 65(1):1–11, 1978.

[251] B. W. Silverman. Some properties of a test for multimodality based on kernel density estimates. In Probability, Statistics and Analysis, volume 79 of London Mathematical Society Lecture Note Series, pages 248–259. Cambridge University Press, Cambridge, 1983.

[252] B. W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall, London, 1986.

[253] P. W. F. Smith, J. J. Forster, and J. W. McDonald. Monte Carlo exact tests for square contingency tables. Journal of the Royal Statistical Society. Series A (Statistics in Society), 159(2):309–321, 1996.

[254] G. Snow. TeachingDemos: Demonstrations for teaching and learning, 2005. R package version 1.5.

[255] C. Spearman. The proof and measurement of association between two things. American Journal of Psychology, 1904.

[256] Student. The probable error of a mean. Biometrika, 6:1–25, 1908.

[257] H. A. Sturges. The choice of a class interval. Journal of the American Statistical Association, 21:65–66, 1926.

[258] G. J. Székely. Potential and kinetic energy in statistics. Lecture notes, Budapest Institute of Technology (Technical University), 1989.

[259] G. J. Székely. E-statistics: Energy of statistical samples. Technical Report 03-05, Bowling Green State University, Department of Mathematics and Statistics, 2000.

[260] G. J. Székely. Student’s t-test for scale mixture errors. In J. Rojo, editor, Optimality, The Second Lehmann Symposium, volume 49 of IMS Lecture Notes – Monograph Series, pages 9–15. Institute of Mathematical Statistics, 2006.

[261] G. J. Székely and M. L. Rizzo. Testing for equal distributions in high dimension. InterStat, 11(5), 2004.

[262] G. J. Székely and M. L. Rizzo. Hierarchical clustering via joint between-within distances: extending Ward’s minimum variance method. Journal of Classification, 22(2):151–183, 2005.

[263] G. J. Székely and M. L. Rizzo. A new test for multivariate normality. Journal of Multivariate Analysis, 93(1):58–80, 2005.

[264] G. J. Székely and M. L. Rizzo. The uncertainty principle of game theory. The American Mathematical Monthly, 8:688–702, October 2007.

[265] G. J. Székely, M. L. Rizzo, and N. K. Bakirov. Measuring and testing dependence by correlation of distances. Annals of Statistics, 35(6), December 2007.

[266] M. A. Tanner. Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions. Third edition, 1993.

[267] M. A. Tanner and W. H. Wong. The calculation of posterior distributions by data augmentation. Journal of the American Statistical Association, 82:528–549, 1987.

[268] T. M. Therneau and B. Atkinson. rpart: Recursive Partitioning, 2007. R port by Brian Ripley. R package version 3.1-36.

[269] R. A. Thisted. Elements of Statistical Computing. Chapman and Hall, New York, 1988.

[270] H. C. Thode, Jr. Testing for Normality. Marcel Dekker, Inc., New York, 2002.

[271] R. Tibshirani and F. Leisch. bootstrap, 2006. Functions for the book “An Introduction to the Bootstrap.” S original by Rob Tibshirani, R port by Friedrich Leisch. R package version 1.0-20.

[272] L. Tierney. Markov chains for exploring posterior distributions (with discussion). Annals of Statistics, 22:1701–1762, 1994.

[273] Y. L. Tong. The Multivariate Normal Distribution. Springer, New York, 1990.

[274] J. Tukey. Bias and confidence in not quite large samples (abstract). Annals of Mathematical Statistics, 29:614, 1958.

[275] J. W. Tukey. Exploratory Data Analysis. Addison-Wesley, New York, 1977.

[276] S. Ulam, R. D. Richtmyer, and J. von Neumann. Statistical methods in neutron diffusion. Los Alamos Scientific Laboratory, report LAMS-551, 1947.

[277] W. N. Venables and B. D. Ripley. S Programming. Springer, New York, 2000.

[278] W. N. Venables and B. D. Ripley. Modern Applied Statistics with S. Springer, New York, fourth edition, 2002. ISBN 0-387-95457-0.

[279] W. N. Venables, D. M. Smith, and the R Development Core Team. An Introduction to R. R Foundation for Statistical Computing, Vienna, Austria, 2007. ISBN 3-900051-12-7.

[280] J. Verzani. Using R for Introductory Statistics. Chapman & Hall/CRC, Boca Raton, FL, 2005.

[281] R. von Mises. Wahrscheinlichkeitsrechnung und Ihre Anwendung in der Statistik und Theoretischen Physik. Deuticke, Leipzig, Germany, 1931.

[282] J. von Neumann. Zur Theorie der Gesellschaftsspiele. Mathematische Annalen, 100:295–320, 1928.

[283] J. von Neumann. Various techniques used in connection with random digits. National Bureau of Standards Applied Mathematics Series, 12:36–38, 1951.

[284] G. Wahba. Spline Models for Observational Data. SIAM, Philadelphia, 1990.

[285] G. G. Walter and X. Shen. Wavelets and other Orthogonal Systems. Chapman & Hall/CRC, Boca Raton, FL, second edition, 2001.

[286] M. Wand and B. Ripley. KernSmooth: Functions for kernel smoothing for Wand & Jones (1995), 2007. S original by Matt Wand. R port by Brian Ripley. R package version 2.22-20.

[287] M. P. Wand. Frequency polygons: Theory and applications. Journal of the American Statistical Association, 80:348–354, 1985.

[288] M. P. Wand. Data-based choice of histogram bin width. The American Statistician, 51:59–64, 1997.

[289] M. P. Wand and M. C. Jones. Kernel Smoothing, volume 60 of Monographs on Statistics and Applied Probability. Chapman and Hall Ltd., London, 1995.

[290] G. R. Warnes. gplots: Various R programming tools for plotting data. Includes R source code and/or documentation contributed by Ben Bolker and Thomas Lumley. R package version 2.3.2.

[291] G. R. Warnes. mcgibbsit: Warnes and Raftery’s MCGibbsit MCMC diagnostic, 2005. R package version 1.0.5.

[292] M. Watanabe and K. Yamaguchi. The EM Algorithm and Related Statistical Models. Marcel Dekker, New York, 2004.

[293] E. Wegman. Nonparametric probability density estimation: I. A summary of available methods. Technometrics, 14:533–546, 1972.

[294] E. Wegman. Hyper dimensional data analysis using parallel coordinates. Journal of the American Statistical Association, 85:664–675, 1990.

[295] E. J. Wegman. Computational statistics: A new agenda for statistical theory and practice. Journal of the Washington Academy of Sciences, 78:310–322, 1988.

[296] S. S. Wilks. On the independence of k sets of normally distributed statistical variables. Econometrica, 3:309–326, 1935.

[297] M. Wiper, D. R. Insua, and F. Ruggeri. Mixtures of gamma distributions with applications. Journal of Computational and Graphical Statistics, 10(3):440–454, 2001.

[298] P. Wolf and U. Bielefeld. aplpack: Another Plot PACKage: stem.leaf, bagplot, faces, spin3R, ..., 2006. R package version 1.0.

[299] D. Wuertz, many others, and see the source file. fSeries: Rmetrics – The Dynamical Process Behind Markets, 2006. R package version 240.10068.