Abe, S. (2001) Pattern Classification: Neuro-Fuzzy Methods and their Comparison, Springer-Verlag.

Abramowitz, Milton and Irene A. Stegun (editors) (1964), Handbook of Mathematical Functions with Formulas, Graphs, and Mathematical Tables, National Bureau of Standards, Washington.

Afifi, A.A. and S.P. Azen (1979), Statistical Analysis: A Computer Oriented Approach, 2d ed., Academic Press, New York.

Agrawal, R. and Srikant, R. (1994), “Fast algorithms for mining association rules,” Proceedings of the 20th International Conference on Very Large Data Bases, Santiago, Chile, August 29 - September 1, 1994.

Agresti, Alan, Dennis Wackerly, and James M. Boyette (1979), Exact conditional tests for cross-classifications: Approximation of attained significance levels, Psychometrika, 44, 75-83.

Aha, D. W. (1991). Incremental constructive induction: An instance-based approach. Proceedings of the Eighth International Workshop on Machine Learning (pp. 117--121). Evanston, ILL: Morgan Kaufmann.

Ahrens, J.H. and U. Dieter (1974), Computer methods for sampling from gamma, beta, Poisson, and binomial distributions, Computing, 12, 223-246.

Ahrens, J.H., and U. Dieter (1985), Sequential random sampling, ACM Transactions on Mathematical Software, 11, 157-169.

Akaike, H., (1978), Covariance Matrix Computation of the State Variable of a Stationary Gaussian Process, Ann. Inst. Statist. Math. 30, Part B, 499-504.

Akaike, H. (1980), Seasonal Adjustment by Bayesian Modeling, Journal of Time Series Analysis, Vol 1, 1-13.

Akaike, H. , Kitagawa, G., Arahata, E., Tada, F., (1979), Computer Science Monographs No. 13, The Institute of Statistical Mathematics, Tokyo.

Anderberg, Michael R. (1973), Cluster Analysis for Applications, Academic Press, New York.

Anderson, T.W. (1971), The Statistical Analysis of Time Series, John Wiley & Sons, New York.

Anderson, T. W. (1994) The Statistical Analysis of Time Series, John Wiley & Sons, New York.

Anderson, R.L. and T.A. Bancroft (1952), Statistical Theory in Research, McGraw-Hill Book Company, New York.

Asuncion, A.and Newman, D.J. (2007), UCI Machine Learning Repository, http://archive.ics.uci.edu/ml/. Irvine, CA: University of California, School of Information and Computer Science.

Atkinson, A.C. (1979), A Family of Switching Algorithms for the Computer Generation of Beta Random Variates, Biometrika, 66, 141-145.

Atkinson, A.C. (1985), Plots, Transformations, and Regression, Claredon Press, Oxford.

Baker, J. E. (1987), Reducing Bias and Inefficiency in the Selection Algorithm. Genetic Algorithms and their Applications: Proceeding of the Second international Conference on Genetic Algorithms, 14-21.

Barrodale, I., and F.D.K. Roberts (1973), An improved algorithm for discrete L1 approximation, SIAM Journal on Numerical Analysis, 10, 839-848.

Barrodale, I., and F.D.K. Roberts (1974), Solution of an overdetermined system of equations in the l1 norm, Communications of the ACM, 17, 319-320.

Barrodale, I., and C. Phillips (1975), Algorithm 495. Solution of an overdetermined system of linear equations in the Chebyshev norm, ACM Transactions on Mathematical Software, 1, 264-270.

Bartlett, M.S. (1935), Contingency table interactions, Journal of the Royal Statistics Society Supplement, 2, 248-252.

Bartlett, M. S. (1937) Some examples of statistical methods of research in agriculture and applied biology, Supplement to the Journal of the Royal Statistical Society, 4, 137-183.

Bartlett, M. (1937), The statistical conception of mental factors, British Journal of Psychology, 28, 97–104.

Bartlett, M.S. (1946), On the theoretical specification and sampling properties of autocorrelated time series, Supplement to the Journal of the Royal Statistical Society, 8, 27–41.

Bartlett, M.S. (1978), Stochastic Processes, 3rd. ed., Cambridge University Press, Cambridge.

Bays, Carter and S.D. Durham (1976), Improving a poor random number generator, ACM Transactions on Mathematical Software, 2, 59-64.

Bendel, Robert B., and M. Ray Mickey (1978), Population correlation matrices for sampling experiments, Communications in Statistics, B7, 163-182.

Berry, M. J. A. and Linoff, G. (1997) Data Mining Techniques, John Wiley & Sons, Inc.

Best, D.J., and N.I. Fisher (1979), Efficient simulation of the von Mises distribution, Applied Statistics, 28, 152-157.

Bishop, C. M. (1995) Neural Networks for Pattern Recognition, Oxford University Press.

Bishop, Yvonne M.M., Stephen E. Feinberg, and Paul W. Holland (1975), Discrete Multivariate Analysis: Theory and Practice, MIT Press, Cambridge, Mass.

Bjorck, Ake, and Gene H. Golub (1973), Numerical Methods for Computing Angles Between Subspaces, Mathematics of Computation, 27, 579-594.

Blom, Gunnar (1958), Statistical Estimates and Transformed Beta-Variables, John Wiley & Sons, New York.

Bosten, Nancy E. and E.L. Battiste (1974), Incomplete beta ratio, Communications of the ACM, 17, 156-157.

Box, George E.P. and Gwilym M. Jenkins (1970) Time Series Analysis: Forecasting and Control, Holden-Day, Inc.

Box, George E.P. and Gwilym M. Jenkins (1976), Time Series Analysis: Forecasting and Control, revised ed., Holden-Day, Oakland.

Box, G.E.P., and David A. Pierce (1970), Distribution of residual autocorrelations in autoregressive-integrated moving average time series models, Journal of the American Statistical Association, 65, 1509–1526.

Box, G.E.P. and P.W. Tidwell (1962), Transformation of the Independent Variables, Technometrics, 4, 531-550.

Box, George E.P., Jenkins,Gwilym M. and Reinsel G.C., (1994) Time Series Analysis, Third edition, Prentice Hall, Englewood Cliffs, New Jersey.

Boyette, James M. (1979), Random RC tables with given row and column totals, Applied Statistics, 28, 329-332.

Bradley, J.V. (1968), Distribution-Free Statistical Tests, Prentice-Hall, New Jersey.

Breiman, L., Friedman, J. H., Olshen, R. A. and Stone, C. J. (1984) Classification and Regression Trees, Chapman & Hall. For the latest information on CART visit: http://www.salford-systems.com/cart.php.

Breslow, N.E. (1974), Covariance analysis of censored survival data, Biometrics, 30, 89-99.

Bridle, J. S. (1990), Probabilistic Interpretation of Feedforward Classification Network Outputs, with relationships to statistical pattern recognition, in F. Fogelman Soulie and J. Herault (Eds.), Neuralcomputing: Algorithms, Architectures and Applications, Springer-Verlag, 227-236.

Brown, Morton E. (1983), MCDP4F, two-way and multiway frequency tables-measures of association and the log-linear model (complete and incomplete tables), in BMDP Statistical Software, 1983 Printing with Additions, (edited by W.J. Dixon), University of California Press, Berkeley.

Brown, Morton B. and Jacqualine K. Benedetti (1977), Sampling behavior and tests for correlation in two-way contingency tables, Journal of the American Statistical Association, 42, 309-315.

Calvo, R. A. (2001), Classifying Financial News with Neural Networks, Proceedings of the 6th Australasian Document Computing Symposium.

Chang, Chih-Chung; Lin, Chih-Jen (2011). "LIBSVM: A library for support vector machines". ACM Transactions on Intelligent Systems and Technology 2 (3).

Chatfield, C., Yar, M. (1988), Holt-Winters Forecasting; Some Practical Issues, J. Royal Stat. Soc., Series D. 7, (2), 129-140..

Chatfield, C., Yar, M. (1991), Prediction intervals for multiplicative Holt-Winters, International Journal of Forecasting. No. 7,31-37.

Chen, C. and Liu, L., Joint Estimation of Model Parameters and Outlier Effects in Time Series, Journal of the American Statistical Association, Vol. 88, No.421, March 1993.

Cheng, R.C.H. (1978), Generating beta variates with nonintegral shape parameters, Communications of the ACM, 21, 317-322.

Chiang, Chin Long (1968), Introduction to Stochastic Processes in Statistics, John Wiley & Sons, New York.

Clarkson, Douglas B. and Robert B Jenrich (1991), Computing extended maximum likelihood estimates for linear parameter models, submitted to Journal of the Royal Statistical Society, Series B, 53, 417-426.

Coley, D. A. (1999), An Introduction to Genetic Algorithms for Scientists and Engineers, World Scientific Publishing Co.

Conover, W.J. (1980), Practical Nonparametric Statistics, 2d ed., John Wiley & Sons, New York.

Conover, W.J. and Ronald L. Iman (1983), Introduction to Modern Business Statistics, John Wiley & Sons, New York.

Conover, W. J., Johnson, M. E., and Johnson, M. M. (1981) A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data, Technometrics, 23, 351-361.

Cook, R. Dennis and Sanford Weisberg (1982), Residuals and Influence in Regression, Chapman and Hall, New York.

Cooper, B.E. (1968), Algorithm AS4, An auxiliary function for distribution integrals, Applied Statistics, 17, 190-192.

Cox, David R. (1970), The Analysis of Binary Data, Methuen, London.

Cox, D.R. (1972), Regression models and life tables (with discussion), Journal of the Royal Statistical Society, Series B, Methodology, 34, 187–220.

Cox, D.R., and P.A.W. Lewis (1966), The Statistical Analysis of Series of Events, Methuen, London.

Cox, D.R., and D. Oakes (1984), Analysis of Survival Data, Chapman and Hall, London.

Cox, D.R., and A. Stuart (1955), Some quick sign tests for trend in location and dispersion, Biometrika, 42, 80-95.

Cranley, R. and Patterson, T.N.L. (1976), Randomization of Number Theoretic Methods for Multiple Integration, SIAM Journal of Numerical Analysis, 13, 904-914.

D'Agostino, Ralph B. and Michael A. Stevens (1986), Goodness-of-Fit Techniques, Marcel Dekker, New York.

Dallal, Gerald E. and Leland Wilkinson (1986), An analytic approximation to the distribution of Lilliefor's test statistic for normality, The American Statistician, 40, 294-296.

Davis, P.J. and Rabinowitz, P. (1984), Methods of Numerical Integration, Academic Press, 482-483.

De Jong, K. A. (1975), An Analysis of the Behavior of a Class of Genetic Adaptive Systems. (Doctorial dissertation, Univ. of Michigan). Dissertation Abstracts International 36(10), 5140B. (University Microfilms No. 76-9381).

Demiroz, G., H. A. Govenir, and N. Ilter (1988), “Learning Differential Diagnosis of Eryhemato-Squamous Diseases using Voting Feature Intervals”, Artificial Intelligence in Medicine.

Dennis, J.E., Jr. and Robert B. Schnabel (1983), Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Prentice-Hall, Englewood Cliffs, New Jersey.

Devore, Jay L (1982), Probability and Statistics for Engineering and Sciences, Brooks/Cole Publishing Company, Monterey, Calif.

Doornik, J.A. (2005), An Improved Ziggurat Method to Generate Normal Random Samples, http://www.doornik.com/research/ziggurat.pdf., University of Oxford.

Draper, N.R. and H. Smith (1981), Applied Regression Analysis, 2d ed., John Wiley & Sons, New York.

Dunnett, C. W. and Sobel, M. (1955), Approximations to the Probability Integral and Certain Percentage Points of a Multivariate analogue of Student’s t-distribution. Biometrika, 42, 258-260.

Durbin, J. (1960), The fitting of time series models, Revue Institute Internationale de Statistics, 28, 233–243.

Efroymson, M.A. (1960), Multiple regression analysis, Mathematical Methods for Digital Computers, Volume 1, (edited by A. Ralston and H. Wilf), John Wiley & Sons, New York, 191-203.

Ekblom, Hakan (1973), Calculation of linear best Lp-approximations, BIT, 13, 292-300.

Ekblom, Hakan (1987), The L1-estimate as limiting case of an Lp or Huber-estimate, in Statistical Data Analysis Based on the L1-Norm and Related Methods (edited by Yadolah Dodge), North-Holland, Amsterdam, 109-116.

Elandt-Johnson, Regina C., and Norman L. Johnson (1980), Survival Models and Data Analysis, John Wiley & Sons, New York, 172-173.

Elman, J. L. (1990) Finding Structure in Time, Cognitive Science, 14, 179-211.

Emmett, W.G. (1949), Factor analysis by Lawless method of maximum likelihood, British Journal of Psychology, Statistical Section, 2, 90-97.

Engle, C. (1982), Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation, Econometrica , 50, 987-1008.

Engle, R.F. and C.W.J. Granger. Long-run Economic Relationships: Readings in Cointegration. Advanced Texts in Econometrics. Oxford University Press. New York, 1991.

Fan, Rong-en, Pai-hsuen Chen and Thorsten Joachims, Working Set Selection Using Second Order Information for Training SVM, Journal of Machine Learning Research, 2005.

Fisher, R.A. (1936), The use of multiple measurements in taxonomic problems, The Annals of Eugenics, 7, 179-188.

Fishman, George S. (1978), Principles of Discrete Event Simulation, John Wiley & Sons, New York.

Fishman, George S. and Louis R. Moore (1982), A statistical evaluation of multiplicative congruential random number generators with modulus , Journal of the American Statistical Association, 77, 129-136.

Forsythe, G.E. (1957), Generation and use of orthogonal polynomials for fitting data with a digital computer, SIAM Journal on Applied Mathematics, 5, 74-88.

Frey, P. W. and D. J. Slate. (1991), “Letter Recognition using Holland-style Adaptive Classifiers”. (Machine Learning Vol 6 #2).

Fuller, Wayne A. (1976), Introduction to Statistical Time Series, John Wiley & Sons, New York.

Furnival, G.M. and R.W. Wilson, Jr. (1974), Regressions by leaps and bounds, Technometrics, 16, 499-511.

Fushimi, Masanori (1990), Random number generation with the recursion Xt = Xt−3p ⊕Xt−3q, Journal of Computational and Applied Mathematics, 31, 105-118.

Gentleman, W. Morven (1974), Basic procedures for large, sparse or weighted linear least squares problems, Applied Statistics, 23, 448-454.

Genz, A. (1992), Numerical Computation of Multivariate Normal Probabilities. J. Comp. Graph Stat., 1, 141-149.

Gibbons, J.D. (1971), Nonparametric Statistical Inference, McGraw-Hill, New York.

Girschick, M.A. (1939), On the Sampling Theory of Roots of Determinantal Equations, Annals of Mathematical Statistics, 10, 203-224.

Gnanadesikan, R. Methods for Statistical Data Analysis of Multivariate Observations. Wiley. New York. (1977).

Goldberg, D. E. (1989), Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing Co.

Goldberg, D. E. and Deb, K. (1991), A Comparative Analysis of Selection Schemes Used in Genetic Algorithms. In G. Rawlins, Ed., Foundations of Genetic Algorithms. Morgan Kaufmann.

Golub, Gene H. and Charles F. Van Loan (1983), Matrix Computations, Johns Hopkins University Press, Baltimore, Md.

Gonin, Rene, and Arthur H. Money (1989), Nonlinear Lp-Norm Estimation, Marcel Dekker, New York.

Goodnight, James H. (1979), A tutorial on the SWEEP operator, The American Statistician, 33, 149-158.

Graybill, Franklin A. (1976), Theory and Application of the Linear Model, Duxbury Press, North Scituate, Mass.

Griffin, R. and K.A. Redish (1970), Remark on Algorithm 347: An efficient algorithm for sorting with minimal storage, Communications of the ACM, 13, 54.

Gross, Alan J., and Virginia A. Clark (1975), Survival Distributions: Reliability Applications in the Biomedical Sciences, John Wiley & Sons, New York.

Gruenberger, F., and A.M. Mark (1951), The d2 test of random digits, Mathematical Tables and Other Aids in Computation, 5, 109-110.

Guerra, Victor O., Richard A. Tapia, and James R. Thompson (1976), A random number generator for continuous random variables based on an interpolation procedure of Akima, Proceedings of the Ninth Interface Symposium on Computer Science and Statistics, (edited by David C. Hoaglin and Roy E. Welsch), Prindle, Weber & Schmidt, Boston, 228-230.

Giudici, P. (2003) Applied Data Mining: Statistical Methods for Business and Industry, John Wiley & Sons, Inc.

Haldane, J.B.S. (1939), The mean and variance of x2 when used as a test of homogeneity, when expectations are small, Biometrika, 31, 346.

Hamilton, James D., Time Series Analysis, Princeton University Press, Princeton (NewJersey), 1994.

Harman, Harry H. (1976), Modern Factor Analysis, 3d ed. revised, University of Chicago Press, Chicago.

Hart, John F., E.W. Cheney, Charles L. Lawson, Hans J. Maehly, Charles K. Mesztenyi, John R. Rice, Henry G. Thacher, Jr., and Christoph Witzgall (1968), Computer Approximations, John Wiley & Sons, New York.

Hartigan, John A. (1975), Clustering Algorithms, John Wiley & Sons, New York.

Hartigan, J.A. and M.A. Wong (1979), Algorithm AS 136: A K-means clustering algorithm, Applied Statistics, 28, 100-108.

Hastie, Trevor, Tibshirani, Robert, and Friedman, Jerome (2009).  The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd ed.  Springer, New York.

Hayter, Anthony J. (1984), A proof of the conjecture that the Tukey-Kramer multiple comparisons procedure is conservative, Annals of Statistics, 12, 61-75.

Hebb, D. O. (1949) The Organization of Behaviour: A Neuropsychological Theory, John Wiley.

Heiberger, Richard M. (1978), Generation of random orthogonal matrices, Applied Statistics, 27, 199-206.

Hemmerle, William J. (1967), Statistical Computations on a Digital Computer, Blaisdell Publishing Company, Waltham, Mass.

Herraman, C. (1968), Sums of squares and products matrix, Applied Statistics, 17, 289-292.

Hill, G.W. (1970), Student's t-distribution, Communications of the ACM, 13, 617-619.

Hill, G.W. (1970), Student's t-quantiles, Communications of the ACM, 13, 619-620.

Hinkelmann, K and Kemthorne, O (1994) Design and Analysis of Experiments – Vol 1, John Wiley.

Hinkley, David (1977), On quick choice of power transformation, Applied Statistics, 26, 67-69.

Hoaglin, David C. and Roy E. Welsch (1978), The hat matrix in regression and ANOVA, The American Statistician, 32, 17-22.

Hocking, R.R. (1972), Criteria for selection of a subset regression: Which one should be used?, Technometrics, 14, 967-970.

Hocking, R.R. (1973), A discussion of the two-way mixed model, The American Statistician, 27, 148-152.

Hocking, R.R. (1985), The Analysis of Linear Models, Brooks/Cole Publishing Company, Monterey, California.

Hollmén, Jaakko, “Process Modeling Using the Self-Organizing Map,” 15.2.1996, Helsinki University of Technology.

Hopfield, J. J. (1987) Learning Algorithms and Probability Distributions in Feed-Forward and Feed-Back Networks, Proceedings of the National Academy of Sciences, 84, 8429-8433.

Holland, J.H. (1975), Adaptation in Natural and Artificial Systems. Ann Arbor: The University of Michigan Press.

Hosmer, D W. and Lemeshow, S (2000), Applied Logistic Regression, 2nd ed., John Wiley & Sons, New York.

Huber, Peter J. (1981), Robust Statistics, John Wiley & Sons, New York.

Hutchinson, J. M. (1994) A Radial Basis Function Approach to Financial Timer Series Analysis, Ph.D. dissertation, Massachusetts Institute of Technology.

Hwang, J. T. G. and Ding, A. A. (1997) Prediction Intervals for Artificial Neural Networks, Journal of the American Statistical Society, 92(438) 748-757.

Iman, R.L., and J.M. Davenport (1980), Approximations of the critical region of the Friedman statistic, Communications in Statistics, A9(6), 571-595.

Jacobs, R. A., Jorday, M. I., Nowlan, S. J., and Hinton, G. E. (1991) Adaptive Mixtures of Local Experts, Neural Computation, 3(1), 79-87.

Jennrich, R.I. and S.M. Robinson (1969), A Newton-Raphson algorithm for maximum likelihood factor analysis, Psychometrika, 34, 111-123.

Jennrich, R.I. and P.F. Sampson (1966), Rotation for simple loadings, Psychometrika, 31, 313-323.

Johansen, S. (1988). Statistical Analysis of Cointegration Vectors. Journal of Economic Dynamics and Control. v 12 , pp 231-54.

Johansen, S. (1995). Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. Oxford University Press, Oxford.

John, Peter W.M. (1971), Statistical Design and Analysis of Experiments, Macmillan Company, New York.

Jöhnk, M.D. (1964), Erzeugung von Betaverteilten und Gammaverteilten Zufallszahlen, Metrika, 8, 5-15.

Johnson, Norman L., and Samuel Kotz (1969), Discrete Distributions, Houghton Mifflin Company, Boston.

Johnson, Norman L., and Samuel Kotz (1970a), Continuous Univariate Distributions-1, John Wiley & Sons, New York.

Johnson, Norman L., and Samuel Kotz (1970b), Continuous Univariate Distributions-2, John Wiley & Sons, New York.

Johnson, N.L. and Kotz, S. (1972), Distributions in Statistics: Continuous Multivariate Distributions, John Wiley & Sons, Inc., New York.

Johnson, D.G., and W.J. Welch (1980), The generation of pseudo-random correlation matrices, Journal of Statistical Computation and Simulation, 11, 55-69.

Jonckheere, A.R. (1954), A distribution-free k-sample test against ordered alternatives, Biometrika, 41, 133-143.

Jöreskog, K.G. (1977), Factor analysis by least squares and maximum-likelihood methods, Statistical Methods for Digital Computers, (edited by Kurt Enslein, Anthony Ralston, and Herbert S. Wilf), John Wiley & Sons, New York, 125-153.

Kachitvichyanukul, Voratas (1982), Computer generation of Poisson, binomial, and hypergeometric random variates, Ph.D. dissertation, Purdue University, West Lafayette, Indiana.

Kaiser, H.F. (1963), Image analysis, Problems in Measuring Change, (edited by C. Harris), University of Wisconsin Press, Madison, Wis.

Kaiser, H.F. and J. Caffrey (1965), Alpha factor analysis, Psychometrika, 30, 1-14.

Kalbfleisch, John D., and Ross L. Prentice (1980), The Statistical Analysis of Failure Time Data, John Wiley & Sons, New York.

Kass, G.V. An Exploratory Technique for Investigating Large Quantities of Categorical Data, Applied Statistics, Vol. 29, No. 2 (1980), pp. 119-127.

Keast, P. (1973) Optimal Parameters for Multidimensional Integration, SIAM Journal of Numerical Analysis, 10, 831-838.

Kemp, A.W., (1981), Efficient generation of logarithmically distributed pseudo-random variables, Applied Statistics, 30, 249-253.

Kendall, Maurice G. and Alan Stuart (1973), The Advanced Theory of Statistics, Volume 2: Inference and Relationship, 3d ed., Charles Griffin & Company, London.

Kendall, Maurice G. and Alan Stuart (1979), The Advanced Theory of Statistics, Volume 2: Inference and Relationship, 4th ed., Oxford University Press, New York.

Kendall, Maurice G., Alan Stuart, and J. Keith Ord (1983), The Advanced Theory of Statistics, Volume 3: Design and Analysis, and Time Series, 4th ed., Oxford University Press, New York.

Kennedy, William J., Jr. and James E. Gentle (1980), Statistical Computing, Marcel Dekker, New York.

Kohonen, T. (1995), Self-Organizing Maps, Third Edition. Springer Series in Information Sciences., New York.

Kuehl, R. O. (2000) Design of Experiments: Statistical Principles of Research Design and Analysis, 2nd edition, Duxbury Press.

Kim, P.J., and R.I. Jennrich (1973), Tables of the exact sampling distribution of the two sample Kolmogorov-Smirnov criterion Dmn (m < n), in Selected Tables in Mathematical Statistics, Volume 1, (edited by H. L. Harter and D.B. Owen), American Mathematical Society, Providence, Rhode Island.

Kinderman, A.J., and J.G. Ramage (1976), Computer generation of normal random variables, Journal of the American Statistical Association, 71, 893-896.

Kinderman, A.J., J.F. Monahan, and J.G. Ramage (1977), Computer methods for sampling from Student’s t distribution, Mathematics of Computation 31, 1009-1018.

Kinnucan, P. and H. Kuki (1968), A Single Precision INVERSE Error Function Subroutine, Computation Center, University of Chicago.

Kirk, Roger E. (1982), Experimental Design: Procedures for the Behavioral Sciences, 2d ed., Brooks/Cole Publishing Company, Monterey, Calif.

Kitagawa, G. and Akaike, H., A Procedure for the modeling of non-stationary time series, Ann. Inst. Statist. Math. 30 (1978), Part B, 351-363.

Konishi, S. and Kitagawa, G (2008), Information Criteria and Statistical Modeling, Springer, New York.

Knuth, Donald E. (1981), The Art of Computer Programming, Volume 2: Seminumerical Algorithms, 2d ed., Addison-Wesley, Reading, Mass.

Kshirsagar, Anant M. (1972), Multivariate Analysis, Marcel Dekker, New York.

Lachenbruch, Peter A. (1975), Discriminant Analysis, Hafner Press, London.

Lai, D. (1998a), Local asymptotic normality for location-scale type processes. Far East Journal of Theorectical Statistics, (in press).

Lai, D. (1998b), Asymptotic distributions of the correlation integral based statistics. Journal of Nonparametric Statistics, (in press).

Lai, D. (1998c), Asymptotic distributions of the estimated BDS statistic and residual analysis of AR Models on the Canadian lynx data. Journal of Biological Systems, (in press).

Laird, N.M., and D. Fisher (1981), Covariance analysis of censored survival data using log-linear analysis techniques, JASA 76, 1231-1240.

Lawless, J.F. (1982), Statistical Models and Methods for Lifetime Data, John Wiley & Sons, New York.

Lawley, D.N. and A.E. Maxwell (1971), Factor Analysis as a Statistical Method, 2d ed., Butterworth, London.

Lawrence, S., Giles, C. L, Tsoi, A. C., Back, A. D. (1997) Face Recognition: A Convolutional Neural Network Approach, IEEE Transactions on Neural Networks, Special Issue on Neural Networks and Pattern Recognition, 8(1), 98-113.

Learmonth, G.P. and P.A.W. Lewis (1973), Naval Postgraduate School Random Number Generator Package LLRANDOM, NPS55LW73061A, Naval Postgraduate School, Monterey, Calif.

Lee, Elisa T. (1980), Statistical Methods for Survival Data Analysis, Lifetime Learning Publications, Belmont, Calif.

Lehmann, E.L. (1975), Nonparametrics: Statistical Methods Based on Ranks, Holden-Day, San Francisco.

Levenberg, K. (1944), A method for the solution of certain problems in least squares, Quarterly of Applied Mathematics, 2, 164-168.

Levene, H. (1960) In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling, I. Olkin et al. editors, Stanford University Press, 278-292.

Lewis, P.A.W., A.S. Goodman, and J.M. Miller (1969), A pseudorandom number generator for the System/360, IBM Systems Journal, 8, 136-146.

Li, L. K. (1992) Approximation Theory and Recurrent Networks, Proc. Int. Joint Conf. On Neural Networks, vol. II, 266-271.

Lilliefors, H.W. (1967), On the Kolmogorov-Smirnov test for normality with mean and variance unknown, Journal of the American Statistical Association, 62, 534-544.

Lippmann, R. P. (1989) Review of Neural Networks for Speech Recognition, Neural Computation, I, 1-38.

Ljung, G.M., and G.E.P. Box (1978), On a measure of lack of fit in time series models, Biometrika, 65, 297–303.

Loh, W.-Y. and Shih, Y.-S. (1997) Split Selection Methods for Classification Trees, Statistica Sinica, 7, 815-840. For information on the latest version of QUEST see: http://www.stat.wisc.edu/~loh/quest.html.

Longley, James W. (1967), An appraisal of least-squares programs for the electronic computer from the point of view of the user, Journal of the American Statistical Association, 62, 819-841.

Lutkepohl, New Introduction to Multiple Time Series Analysis. Springer. 2007, Chapter 12.

Makoto Matsumoto and Takuji Nishimura, ACM Transactions on Modeling and Computer Simulation, Vol. 8, No. 1, January 1998, Pages 3–30.

Mandic, D. P. and Chambers, J. A. (2001) Recurrent Neural Networks for Prediction, John Wiley & Sons, LTD.

Manning, C. D. and Schütze, H. (1999) Foundations of Statistical Natural Language Processing, MIT Press.

Marsaglia, George (1964), Generating a variable from the tail of a normal distribution, Technometrics, 6, 101-102.

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

Marsaglia, G. (1972), The structure of linear congruential sequences, in Applications of Number Theory to Numerical Analysis, (edited by S. K. Zaremba), Academic Press, New York, 249-286.

Marsaglia, George (1972), Choosing a point from the surface of a sphere, The Annals of Mathematical Statistics, 43, 645-646.

Marsaglia, G. and Tsang, W. W. (2000), The Ziggurat Method for Generating Random Variables, Journal of Statistical Software, 5-8, 1-7.

McCulloch, W. S. and Pitts, W. (1943), A Logical Calculus for Ideas Imminent in Nervous Activity, Bulletin of Mathematical Biophysics, 5, 115-133.

McKean, Joseph W., and Ronald M. Schrader (1987), Least absolute errors analysis of variance, in Statistical Data Analysis Based on the L1-Norm and Related Methods (edited by Yadolah Dodge), North-Holland, Amsterdam, 297-305.

McCullagh, P., and J.A. Nelder, (1983), Generalized Linear Models, Chapman and Hall, London.

Maindonald, J.H. (1984), Statistical Computation, John Wiley & Sons, New York.

Marazzi, Alfio (1985), Robust affine invariant covariances in ROBETH, ROBETH-85 document No. 6, Division de Statistique et Informatique, Institut Universitaire de Medecine Sociale et Preventive, Laussanne.

Mardia, K.V. (1970), Measures of multivariate skewness and kurtosis with applications, Biometrics, 57, 519-530.

Mardia, K.V., J.T. Kent, J.M. Bibby (1979), Multivariate Analysis, Academic Press, New York.

Mardia, K.V. and K. Foster (1983), Omnibus tests of multinormality based on skewness and kurtosis, Communications in Statistics A, Theory and Methods, 12, 207-221.

Marquardt, D. (1963), An algorithm for least-squares estimation of nonlinear parameters, SIAM Journal on Applied Mathematics, 11, 431-441.

Marsaglia, George (1964), Generating a variable from the tail of a normal distribution, Technometrics, 6, 101-102.

Marsaglia, G. and T.A. Bray (1964), A convenient method for generating normal variables, SIAM Review, 6, 260-264.

Marsaglia, G., M.D. MacLaren, and T.A. Bray (1964), A fast procedure for generating normal random variables, Communications of the ACM, 7, 4-10.

Merle, G., and H. Spath (1974), Computational experiences with discrete Lp approximation, Computing, 12, 315-321.

Miller, Rupert G., Jr. (1980), Simultaneous Statistical Inference, 2d ed., Springer-Verlag, New York.

Milliken, George A. and Dallas E. Johnson (1984), Analysis of Messy Data, Volume 1: Designed Experiments, Van Nostrand Reinhold, New York.

Mitchell, M. (1996), An Introduction to Genetic Algorithms, MIT Press.

Moran, P.A.P. (1947), Some theorems on time series I, Biometrika, 34, 281-291.

Moré, Jorge, Burton Garbow, and Kenneth Hillstrom (1980), User Guide for [4] MINPACK-1, Argonne National Laboratory Report ANL-80_74, Argonne, Ill.

Morrison, Donald F. (1976), Multivariate Statistical Methods, 2nd. ed. McGraw-Hill Book Company, New York.

Muller, M.E. (1959), A note on a method for generating points uniformly on N‑dimensional spheres, Communications of the ACM, 2, 19-20.

Nelson, D. B. (1991), Conditional heteroskedasticity in asset returns: A new approach. Econometrica, , 59, 347-370.

Nelson, Peter (1989), Multiple Comparisons of Means Using Simultaneous Confidence Intervals, Journal of Quality Technology, 21, 232-241.

Neter, John (1983), Applied Linear Regression Models, Richard D. Irwin, Homewood, Ill.

Neter, John and William Wasserman (1974), Applied Linear Statistical Models, Richard D. Irwin, Homewood, Ill.

Noether, G.E. (1956), Two sequential tests against trend, Journal of the American Statistical Association, 51, 440-450.

NVIDIA Corporation (©2005-2011), © All rights reserved. Portions of the NVIDIA SGEMM and DGEMM library routines were written by Vasily Volkov and are subject to the Modified Berkeley Software Distribution License. (©) 2007-09, Regents of the University of California.

Otto, M.C., Bell, W.R. and Burman, J.P. (1987), “An Iterative GLS Approach to Maximum Likelihood Estimation of Regression Models With ARIMA Errors,” American Statistical Association, Proceedings of the Business and Economics Statistics Section, 632-637.

Owen, D.B. (1962), Handbook of Statistical Tables, Addison-Wesley Publishing Company, Reading, Mass.

Owen, D.B. (1965), A Special Case of the Bivariate Non-central t Distribution, Biometrika, 52, 437-446.

Ozaki, T and Oda H (1978) Nonlinear time series model identification by Akaike's information criterion. Information and Systems, Dubuisson eds, Pergamon Press. 83-91.

Pao, Y. (1989) Adaptive Pattern Recognition and Neural Networks, Addison-Wesley Publishing.

Palm, F. C. (1996), GARCH models of volatility. In Handbook of Statistics, Vol. 14, 209-240. Eds: Maddala and Rao. Elsevier,New York.

Parker, D. B., (1985), Learning Logic. Technical Report TR-47, Cambridge, MA: MIT Center for Research in computational Economics and Management Science.

Patefield, W.M. (1981), An efficient method of generating R × C tables with given row and column totals, Applied Statistics, 30, 91-97.

Patefield, W.M. (1981), and Tandy D. (2000) Fast and Accurate Calculation of Owen’s T‑Function, J. Statistical Software, 5, Issue 5.

Peixoto, Julio L. (1986), Testable hypotheses in singular fixed linear models, Communications in Statistics: Theory and Methods, 15, 1957-1973.

Petro, R. (1970), Remark on Algorithm 347: An efficient algorithm for sorting with minimal storage, Communications of the ACM, 13, 624.

Pillai, K.C.S. (1985), Pillai’s trace, in Encyclopedia of Statistical Sciences, Volume 6, (edited by Samuel Kotz and Norman L. Johnson), John Wiley & Sons, New York, 725-729.

Poli, I. and Jones, R. D. (1994) A Neural Net Model for Prediction, Journal of the American Statistical Society, 89(425) 117-121.

Pregibon, Daryl (1981), Logistic regression diagnostics, The Annals of Statistics, 9, 705-724.

Prentice, Ross L. (1976), A generalization of the probit and logit methods for dose response curves, Biometrics, 32, 761-768.

Priestley, M.B. (1981), Spectral Analysis and Time Series, Volumes 1 and 2, Academic Press, New York.

Quinlan, J. R. (1993). C4.5 Programs for Machine Learning, Morgan Kaufmann. For the latest information on Quinlan’s algorithms see http://www.rulequest.com/.

Quinlan (1987). Simplifying Decision Trees. Int J Man-Machine Studies 27, pp. 221-234.

Rajaraman Anand and Ullman, Jeffrey David (2011), Mining of Massive Datasets, Cambridge University Press, Cambridge, UK.

Rao, C. Radhakrishna (1973), Linear Statistical Inference and Its Applications, 2d ed., John Wiley & Sons, New York.

Reed, R. D. and Marks, R. J. II (1999) Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks, The MIT Press, Cambridge, MA.

Ripley, B. D. (1994) Neural Networks and Related Methods for Classification, Journal of the Royal Statistical Society B, 56(3), 409-456.

Ripley, B. D. (1996) Pattern Recognition and Neural Networks, Cambridge University Press.

Robinson, Enders A. (1967), Multichannel Time Series Analysis with Digital Computer Programs, Holden-Day, San Francisco.

Rosenblatt, F. (1958) The Perceptron: A Probabilistic Model for Information Storage and Organization in the Brain, Psychol. Rev., 65, 386-408.

Royston, J.P. (1982a), An extension of Shapiro and Wilk's W test for normality to large samples, Applied Statistics, 31, 115-124.

Royston, J.P. (1982b), The W test for normality, Applied Statistics, 31, 176-180.

Royston, J.P. (1982c), Expected Normal Order Statistics (exact and approximate), Applied Statistics, 31, 161-165.

Royston, J. P. (1991), Approximating the Shapiro-Wilk W-test for non-normality, Statistics and Computing, 2, 117-119.

Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986) Learning Representations by Back-Propagating Errors, Nature, 323, 533-536.

Rumelhart, D. E. and McClelland, J. L. eds. (1986) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, 1, 318-362, MIT Press.

Sallas, William M. (1990), An algorithm for an Lp norm fit of a multiple linear regression model, American Statistical Association 1990 Proceedings of the Statistical Computing Section, 131-136.

Sallas, William M. and Abby M. Lionti (1988), Some useful computing formulas for the nonfull rank linear model with linear equality restrictions, IMSL Technical Report 8805, IMSL, Houston.

Savage, I. Richard (1956), Contributions to the theory of rank order statistics-the two-sample case, Annals of Mathematical Statistics, 27, 590-615.

Savasere, Ashok; Omiecinski, Edward; and Navathe, Shamkant (1995), “An Efficient Algorithm for Mining Association Rules in Large Databases”, Proceedings of the 21st International Conference on Very Large Data Bases, Zurich, Switzerland, 1995

Scheffe, Henry (1959), The Analysis of Variance, John Wiley & Sons, New York.

Schmeiser, Bruce (1983), Recent advances in generating observations from discrete random variates, Computer Science and Statistics: Proceedings of the Fifteenth Symposium on the Interface, (edited by James E. Gentle), North-Holland Publishing Company, Amsterdam, 154-160.

Schmeiser, Bruce W. and A.J.G. Babu (1980), Beta variate generation via exponential majorizing functions, Operations Research, 28, 917-926.

Schmeiser, Bruce and Voratas Kachitvichyanukul (1981), Poisson Random Variate Generation, Research Memorandum 81-4, School of Industrial Engineering, Purdue University, West Lafayette, Ind.

Schmeiser, Bruce W. and Ram Lal (1980), Squeeze methods for generating gamma variates, Journal of the American Statistical Association, 75, 679-682.

Searle, S.R. (1971), Linear Models, John Wiley & Sons, New York.

Seber, G.A.F. (1984), Multivariate Observations, John Wiley & Sons, New York.

Shampine, L.F. (1975), Discrete least-squares polynomial fits, Communications of the ACM, 18, 179-180.

Siegal, Sidney (1956), Nonparametric Statistics for the Behavioral Sciences, McGraw-Hill, New York.

Singleton, R.C. (1969), Algorithm 347: An efficient algorithm for sorting with minimal storage, Communications of the ACM, 12, 185-187.

Smirnov, N.V. (1939), Estimate of deviation between empirical distribution functions in two independent samples (in Russian), Bulletin of Moscow University, 2, 3-16.

Smith, H., and S. D. Dubey (1964), "Some reliability problems in the chemical industry", Industrial Quality Control, 21 (2), 1964, 64-70.

Smith, M. (1993) Neural Networks for Statistical Modeling, New York: Van Nostrand Reinhold.

Snedecor, George W. and William G. Cochran (1967), Statistical Methods, 6th ed., Iowa State University Press, Ames, Iowa.

Snedecor, George W. and Cochran, William G. (1967) Statistical Methods, 6th edition, Iowa State University Press, 296-298.

Snedecor, George W. and Cochran, William G. (1967) Statistical Methods, 6th edition, Iowa State University Press, 432-436.

Sposito, Vincent A. (1989), Some properties of Lp-estimators, in Robust Regression: Analysis and Applications (edited by Kenneth D. Lawrence and Jeffrey L. Arthur), Marcel Dekker, New York, 23-58.

Spurrier, John D. and Steven P. Isham (1985), Exact simultaneous confidence intervals for pairwise comparisons of three normal means, Journal of the American Statistical Association, 80, 438-442.

Stablein, D.M, W.H. Carter, and J.W. Novak (1981), Analysis of survival data with nonproportional hazard functions, Controlled Clinical Trials, 2, 149–159.

Stahel, W. (1981), Robuste Schatzugen: Infinitesimale Opimalitat und Schatzugen von Kovarianzmatrizen, Dissertation no. 6881, ETH, Zurich.

Steel and Torrie (1960) Principles and Procedures of Statistics, McGraw-Hill.

Stephens, M.A. (1974), EDF statistics for goodness of fit and some comparisons, Journal of the American Statistical Association, 69, 730-737.

Stephens, M.A. (1986): Tests based on EDF statistics. In: D’Agostino, R.B. and Stephens, M.A., eds.: Goodness-of-Fit Techniques. Marcel Dekker, New York.

Stirling, W.D. (1981), Least squares subject to linear constraints, Applied Statistics, 30, 204-212. (See correction, p. 357.)

Stoline, Michael R. (1981), The status of multiple comparisons: simultaneous estimation of all pairwise comparisons in one-way ANOVA designs, The American Statistician, 35, 134-141.

Storey, John D. (2003).  “The Positive False Discovery Rate: A Bayesian Interpretation and the q‑value.”  The Annals of Statistics.  Vol. 31, No. 6, pp 2013-2035.

Storey, John D. (2002). “A Direct Approach to False Discovery Rates.” Journal of the Royal Statistical Society, Series B. 64, part 3, pp 479-498.

Storey, John D. and Robert Tibshirani  (2003). “Statistical Significance for Genomewide Studies.”  PNAS.  Vol. 100, No. 16. pp 9440-9445.

Strecok, Anthony J. (1968), On the calculation of the inverse of the error function, Mathematics of Computation, 22, 144-158.

Studenmund, A. H. (1992) Using Economics: A Practical Guide, New York: Harper Collins.

Swingler, K. (1996) Applying Neural Networks: A Practical Guide, Academic Press.

Tanner, Martin A., and Wing H. Wong (1983), The estimation of the hazard function from randomly censored data by the kernel method, Annals of Statistics, 11, 989–993.

Tanner, Martin A., and Wing H. Wong (1984), Data-based nonparametric estimation of the hazard function with applications to model diagnostics and exploratory analysis, Journal of the American Statistical Association, 79, 123–456.

Taylor, Malcolm S., and James R. Thompson (1986), Data based random number generation for a multivariate distribution via stochastic simulation, Computational Statistics & Data Analysis, 4, 93-101.

Tesauro, G. (1990) Neurogammon Wins Computer Olympiad, Neural Computation, 1, 321-323.

Tezuka, S. (1995), Uniform Random Numbers: Theory and Practice. Academic Publishers, Boston.

Thisted, Ronald. A. (1988).  Elements of Statistical Computing: Numerical Computation. Chapman & Hall, New York.

Thompson, James R, (1989), Empirical Model Building, John Wiley & Sons, New York.

Tong, Y. L. (1990), The Multivariate Normal Distribution, Springer-Verlag, New York.

Tucker, Ledyard and Charles Lewis (1973), A reliability coefficient for maximum likelihood factor analysis, Psychometrika, 38, 1-10.

Tukey, John W. (1962), The future of data analysis, Annals of Mathematical Statistics, 33, 1-67.

Velleman, Paul F. and David C. Hoaglin (1981), Applications, Basics, and Computing of Exploratory Data Analysis, Duxbury Press, Boston.

Verdooren, L. R. (1963), Extended tables of critical values for Wilcoxon's test statistic, Biometrika, 50, 177-186.

Wallace, D.L. (1959), Simplified Beta-approximations to the Kruskal-Wallis H-test, Journal of the American Statistical Association, 54, 225-230.

Warner, B. and Misra, M. (1996) Understanding Neural Networks as Statistical Tools, The American Statistician, 50(4) 284-293.

Weisberg, S. (1985), Applied Linear Regression, 2d ed., John Wiley & Sons, New York.

Werbos, P. (1974) Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Science, PhD thesis, Harvard University, Cambridge, MA.

Werbos, P. (1990) Backpropagation Through Time: What It Does and How to do It, Proc. IEEE, 78, 1550-1560.

Wetzel, A. (1983), Evaluation of the Effectiveness of Genetic Algorithms in Combinatorial Optimization, Unpublished manuscript, Univ. of Pittsburg, Pittsburg.

Williams, R. J. and Zipser, D. (1989) A Learning Algorithm for Continuously Running Fully Recurrent Neural Networks, Neural Computation, 1, 270-280.

Witten, I. H. and Frank, E. (2000) Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations, Morgan Kaufmann Publishers.

Woodfield, Terry J. (1990), Some notes on the Ljung-Box portmanteau statistic, American Statistical Association 1990 Proceedings of the Statistical Computing Section, 155–160.

Wu, S-I (1995) Mirroring Our Thought Processes, IEEE Potentials, 14, 36-41.

Ruibin X., Lin N., and Chen Y., (2008), “Compression and Aggregation for Logistic Regression Analysis in Data Cubes,”  IEEE Transactions on Knowledge and Data Engineering. Vol. 1, No. 1.

Yates, F. (1936) A new method of arranging variety trials involving a large number of varieties. Journal of Agricultural Science, 26, 424-455.

Benjamini, Y., Hochberg, Y., (1995), “Controlling the False Discovery Rate: A Practical and Powerful approach to Multiple Testing.”   Journal of the Royal Statistical Society, Series B.  Vol. 57, No. 1., pp . 289-300.