References

Abe

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

Abramowitz and Stegun

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

Afifi and Azen

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

Agrawal and Srikant

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, Wackerly, and Boyette

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

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 and Dieter

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

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 et al

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

Anderberg

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

Anderson

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 and Bancroft

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

Asuncion and Newman

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

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

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 and Roberts

Barrodale, I., and F.D.K. Roberts (1973), An improved algorithm for discrete \(L_1\) 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 \(l_1\) 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.

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 and Durham

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

Bendel and Mickey

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

Berry

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

Best and Fisher

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

Bishop

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

Bishop et al

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

Bjorck and Golub

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

Blom

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

Bosten and Battiste

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

Box and Jenkins

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 and Pierce

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 and Tidwell

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

Box et al.

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

Boyette

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

Bradley

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

Breiman

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

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

Bridel

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

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 and Benedetti

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

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

Chang and Lin

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

Chatfield and Yar

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 and Liu

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

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

Chiang

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

Clarkson and Jenrich

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

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

Conover

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

Conover and Iman

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

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 and Weisberg

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

Cooper

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

Cox

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 and Lewis

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

Cox and Oakes

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

Cox and Stuart

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

Cranley and Patterson

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 and Stevens

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

Dallal and Wilkinson

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 and Rabinowitz

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

De Jong

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 et al.

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 and Schnabel

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

Devore

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

Doornik

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

Draper and Smith

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

Dunnett and Sobel

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

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

Efroymson

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

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

Ekblom, Hakan (1987), The \(L_1\)-estimate as limiting case of an \(L_p\) or Huber-estimate, in Statistical Data Analysis Based on the \(L_1\)-Norm and Related Methods (edited by Yadolah Dodge), North-Holland, Amsterdam, 109-116.

Elandt-Johnson and Johnson

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

Elman

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

Emmett

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

Engle

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

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, Chen, and Joachims

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

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

Fishman

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

Fishman and Moore

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

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 and Slate

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

Fuller

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

Furnival and Wilson

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

Fushimi

Fushimi, Masanori (1990), Random number generation with the recursion \(X_t=X_{t-3p}\bigoplus X_{t-3q}\), Journal of Computational and Applied Mathematics, 31, 105-118.

Gentleman

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

Genz

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

Gibbons

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

Girschick

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

Gnanadesikan

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

Goldberg

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 and Van Loan

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

Gonin and Money

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

Goodnight

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

Graybill

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

Griffin and Redish

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 and Clark

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

Gruenberger and Mark

Gruenberger, F., and A.M. Mark (1951), The \(d^2\) test of random digits, Mathematical Tables and Other Aids in Computation, 5, 109-110.

Guerra et al.

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

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

Haldane

Haldane, J.B.S. (1939), The mean and variance of \(x^2\) when used as a test of homogeneity, when expectations are small, Biometrika, 31, 346.

Hamilton

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

Harman

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

Hart et al

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

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

Hartigan and Wong

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

Hastie et al

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

Hayter

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

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

Heiberger

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

Hemmerle.

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

Herraman

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

Hill

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

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

Hinkley

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

Hoaglin and Welsch

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

Hocking

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

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

Hopfield

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

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

Hosmer and Lemeshow

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

Huber

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

Hutchinson

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

Hughes and Saw

Hughes, David T., and John G. Saw (1972), Approximating the percentage points of Hotelling’s generalized

\[T_0^2\]

statistic, Biometrika, 59, 224-226.

Hwang

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 and Davenport

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

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 and Robinson

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

Jennrich and Sampson

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

Johansen

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

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

Jöhnk

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

Johnson and Kotz

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 and Kotz

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

Johnson and Welch

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

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

Jöreskog

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

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

Kaiser

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

Kaiser and Caffrey

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

Kalbfleisch and Prentice

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

Kass

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

Keast

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

Kemp

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

Kendall and Stuart

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 et al.

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 and Gentle

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

Kohonen

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

Kuehl, R. O.

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

Kim and Jennrich

Kim, P.J., and R.I. Jennrich (1973), Tables of the exact sampling distribution of the two sample Kolmogorov-Smirnov criterion \(D_{mn}\) (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 and Ramage

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

Kinderman et al.

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 and Kuki

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

Kirk

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

Kitagawa and Akaike

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 and Kitagawa

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

Knuth

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

Kshirsagar

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

Lachenbruch

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

Lai

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 and Oliver

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

Lawless

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

Lawley and Maxwell

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

Lawrence et al

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 and Lewis

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

Lee

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

Lehmann

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

Levenberg

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

Levene, H.

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 et al.

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

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

Liffiefors

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

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

Ljung and Box

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

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.math.ccu.edu.tw/~yshih/quest.html.

Longley

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.

Lütkepohl

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

Matsumoto and Nishimure

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

Mandic

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

Manning

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

Marsaglia

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 and Tsang

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

McCulloch

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

McKean and Schrader

McKean, Joseph W., and Ronald M. Schrader (1987), Least absolute errors analysis of variance, in Statistical Data Analysis Based on the \(L_1\)-Norm and Related Methods (edited by Yadolah Dodge), North-Holland, Amsterdam, 297-305.

McKeon

McKeon, James J. (1974), F approximations to the distribution of Hotelling’s

\[T_0^2\]

, Biometrika, 61, 381-383.

McCullagh and Nelder

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

Maindonald

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

Marazzi

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 et al.

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 and Foster

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

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

Marsaglia

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

Marsaglia and Bray

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

Marsaglia et al.

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 and Spath

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

Miller

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

Milliken and Johnson

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

Mitchell

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

Moran

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

Moré et al.

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

Morrison

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

Muller

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

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

Nelson

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

Neter

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

Neter and Wasserman

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

Noether

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

NVIDIA

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 et al

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

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 and Oda

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

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

Palm

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

Parker

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

Patefield

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

Patefield and Tandy

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

Peixoto

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

Petro

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

Pillai

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

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

Pregibon

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

Prentice

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

Priestley

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

Quinlan

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 and Ullman

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

Rao

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

Reed

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

Ripley

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

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

Rosenblatt

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

Royston

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

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

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

Sallas and Lionti

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

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

Savasere, Omiecinski, and Navathe

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

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

Schmeiser

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 and Babu

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

Schmeiser and Kachitvichyanukul

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

Schmeiser and Lal

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

Searle

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

Seber

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

Shampine

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

Siegal

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

Singleton

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

Smirnov

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 and Dubey

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

Smith

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

Snedecor and Cochran

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

Snedecor and Cochran

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

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

Sposito

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

Spurrier and Isham

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, Carter, and Novak

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

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

Steel and Torrie

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

Stephens

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

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

Stoline

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.

Story

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

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

Studenmund

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

Swingler

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

Tanner and Wong

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 and Thompson

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

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

Tezuka

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

Thisted

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

Thompson

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

Tong

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

Tucker and Lewis

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

Tukey

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

Velleman and Hoaglin

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

Verdooren

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

Wallace

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

Warner

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

Weisberg

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

Werbos

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

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

Williams

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

Witten

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

Woodfield

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

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

Xi et al

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.

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

Yoav and Hochberg

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.