by Peter M Lee
Kelley, T. L., Interpretation of Educational Measurements, Yonkers-on-Hudson, NY: World Book Co. (1927).
Kendall, M. G., and Plackett, R. L. (eds), Studies in the History of Probability and Statistics, Vol. II, London: Griffin (1977).
Kendall, M. G., Stuart, A., and Ord, J. K., The Advanced Theory of Statistics, Vol. I (5th ed.), London: Griffin (1987).
Kennedy, W. G., and Gentle, J. E., Statistical Computing, New York: Marcel Dekker (1980).
Kennett, P., and Ross, C. A., Geochronology, London: Longmans (1983).
Kleinbaum, D. G., Logistic Regression: A Self-Learning Text, Berlin: Springer-Verlag (1994).
Knuth, D. E., The Art of Computer Programming, Vol. 2: Seminumerical Algorithms, Reading, MA: Addison-Wesley (1981) [1st edn (1969)].
Kotz, S., and Johnson, N. L. (eds), Breakthroughs in Statistics (3 vols), Berlin: Springer-Verlag (1992–1997).
Kotz, S., and Nadarajan, S., Multivariate t Distributions, Cambridge: Cambridge University Press (2004).
Kotz, S., Read, C. B., Balakrishnan, N, and Vidakovic, B. (eds), Encyclopedia of Statistical Science (2nd edn in 16 vols), New York: Wiley-Interscience (2006) [1st edn by Kotz, S., and Johnson, N. L.(eds) (1982–1989)]
Krause, A., and Olsen, M, The Basics of S and S-PLUS, Berlin: Springer-Verlag (2000) [1st edn (1997)].
Kullback, S., Information Theory and Statistics, New York: John Wiley & Sons (1959); New York: Dover (1968).
Kullback, S., and Leibler, R. A., On information and sufficiency, Ann. Math. Statist., 22 (1951), 79–86.
Kyburg, H. E., and Smokler, H. E. (eds), Studies in Subjective Probability, New York: John Wiley & Sons (1964) [2nd edn (much altered); Melbourne, FA: Krieger (1980)].
Laplace, P. S., Mémoire sur la probabilité des causes par les évenemens Mém. de math. et phys. presenté à l’Acad. roy. des sci., 6 (1774), 621–686 [reprinted in his Œuvres complètes, 8, 27–65. An English translation is to be found in Stigler (1986b)].
Laplace, P. S., Théorie Analytiques des Probabilités, Paris: Courcier (1812) [reprinted Brussels: Culture et Civilisation (1967); subsequent edn in 1814 and 1820].
Lee, P. M., Not so spurious, Mathematical Gazette, 75 (1991), 200–201.
Lehmann, E. L., Theory of Point Estimation, New York: John Wiley & Sons (1983).
Lehmann, E. L., Testing Statistical Hypotheses (2nd edn), New York: John Wiley & Sons (1986) [1st edn (1959)].
Lenk, P. J., Towards a practicable Bayesian nonparametric density estimator, Biometrika, 78 (1991), 531–543.
Leonard, T., and Hsu, J. S. J., Bayesian Methods: An Introduction for Statisticians and Interdisciplinary Researchers, Cambridge: Cambridge University Press (2001).
Lieberman, G. J., and Owen, D. B., Tables of the Hypergeometric Probability Distribution, Stanford, CA: Stanford University Press (1961).
Lindgren, B. W., Statistical Theory (4th edn), London: Chapman and Hall (1993) [1st edn (1960), 2nd edn (1962), 3rd edn (1968)].
Lindley, D. V., On a measure of the information provided by an experiment, Ann. Math. Statist., 27 (1956), 936–1005.
Lindley, D. V., A statistical paradox, Biometrika, 44 (1957), 187–192.
Lindley, D. V., Introduction to Probability and Statistics from a Bayesian Viewpoint (2 vols—Part I: Probability and Part II: Inference), Cambridge: Cambridge University Press (1965).
Lindley, D. V., Bayesian least squares, Bull. Inst. Internat. Statist., 43 (2) (1969), 152–153.
Lindley, D. V., Bayesian Statistics: A Review, Philadelphia, PA: S.I.A.M.—Society for Industrial and Applied Mathematics (1971a).
Lindley, D. V., The estimation of many parameters (with discussion), in Godambe and Sprott (eds) (1971b).
Lindley, D. V., A problem in forensic science, Biometrika, 64 (1977), 207–213.
Lindley, D. V., and Scott, W. F., New Cambridge Elementary Statistical Tables, Cambridge: Cambridge University Press (1995) [1st edn (1984)].
Lindley, D. V., and Smith, A. F. M., Bayes estimates for the linear model (with discussion), J. Roy. Statist. Soc. Ser. B, 34 (1972), 1–41 [reprinted in Polson and Tiao (1995, Volume II) and in Kotz and Johnson (1992–1997, Volume III)].
Liu, Y. S., Peskun's theorem and a modified discrete-state Gibbs sampler, Biometrika, 83 (1996), 681–682.
Liu, Y. S., Markov Chain Strategies in Scientific Computing, Berlin: Springer-Verlag 2001.
Luce, R. D., Bush, R. B., and Galanter, E., Readings in Mathematical Psychology, Vol. 2, New York: John Wiley & Sons (1965).
Mardia, K. V., Statistics of Directional Data, New York: Academic Press (1972).
Mardia, K. V., and Jupp, P. E., Directional Statistics, New York: John Wiley & Sons (2001).
Marin, J. M., amd Robert, C. P., Bayesian Core: A practical Approach to Computational Bayesian Statistics, New York: Springer-Verlag (2007).
Maritz, J. S., and Lwin, T., Empirical Bayes Methods (2nd edn), London: Methuen (1989) [1st edn by Maritz alone (1970)].
McCullagh, P., and Nelder, J. A., Generalized Linear Models (2nd edn), London: Chapman and Hall (1989) [1st edn (1984)].
McGrayne, S. B., The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from Two Centuries of Controversy, New Haven, CT, and London: Yale University Press (2011).
Mendel, G., Versuche über Pflanzen-Hybriden, Verhandlungen des naturforschenden Vereines in Bürnn, 4 (1865), 3–47 [translation by E. R. Sherwood in Stern and Sherwood (1966)].
Metropolis, N., Rosenbluth, A. W., Rosenbluth, M. N., Teller, A. H., and Teller, E., Equation of state calculations by fast computing machines, J. Chem. Phys., 21 (1953), 1087–1092 [reprinted in Kotz and Johnson (1992–1997, Volume III)].
Meyer, D. L., and Collier, R. O. (eds), Bayesian Statistics, Itasca, IL: F. E. Peacock (1970).
Meyn, S. P., and Tweedie, R. L., Markov Chains and Stochastic Stability, Berlin: Springer-Verlag (1993).
Miller, K. S., Some Eclectic Matrix Theory, Malabar, FL: Krieger (1987).
Morris, C., Parametric empirical Bayes confidence sets: theory and applications, J. Amer. Statist. Assoc., 78 (1983), 47–65.
Müller, P., and Quintana, F. A., Nonparametric Bayesian Data Analysis, Statistical Science, 19 (2004), 95–110.
Nagel, E., Principles of the Theory of Probability, Chicago: University of Chicago Press (1939) [reprinted in Neurath et al. (1955)].
Neapolitan, R. E., Learning Bayesian networks, Upper Saddle River, NJ: Pearson Prentice Hall (2004).
Neave, H. R., Statistics Tables for Mathematicians, Engineers, Economists and the Behavioural and Management Sciences, London: George Allen & Unwin (1978).
Neurath, O., Carnap, R., and Morris, C. (eds), Foundations of the Unity of Science, Vol. I, Chicago: University of Chicago Press (1955).
Newcomb, S., Note on the frequency of use of the different digits in natural numbers, Amer. J. Math., 4 (1881), 39–40 [reprinted in Stigler (1980)].
Norris, J. R., Markov Chains, Cambridge: Cambridge University Press (1997).
Novick, M. R., and Jackson, P. H., Statistical Methods for Educational and Psychological Research, New York: McGraw-Hill (1974).
Ntzoufras, I., Bayesian Methods Using WinBUGS, Hoboken, NJ: John Wiley & Sons (2009).
Odell, P. L., and Feiveson, A. H., A numerical procedure to generate a sample covariance matrix, J. Amer. Statist. Assoc., 61 (1966), 198–203.
O’Hagan, A., and Forster, J., Kendall's Advanced Theory of Statistics: Volume 2B: Bayesian Inference, London: Arnold (2004) [1st edn by O’Hagan alone (1994)].
Ormerod, J. T., and Wand, M. P., Explaining Variational Approximations, American Statistician, 64 (2010), 140–153.
Ó Ruanaidh, J. J. K., and Fitzgerald, W. J., Numerical Bayesian Methods applied to Signal Processing, Berlin: Springer-Verlag (1996).
Patil, V. H., Approximations to the Behrens-Fisher distribution, Biometrika, 52 (1965), 267–271.
Pearson, E. S. (ed. Plackett, R. L. and Barnard, H. A.
), ‘Student’: A Statistical Biography of William Sealy Gosset, Oxford: University Press (1990).
Pearson, E. S., and Hartley, H. O., Biometrika Tables for Statisticians (2 vols), Cambridge: Cambridge University Press for Biometrika (Vol. I—1954, 1958, 1966; Vol. II—1972).
Pearson, E. S., and Kendall, M. G., Studies in the History of Probability and Statistics, London: Griffin (1970).
Pearson, K., Tables of the Incomplete Gamma Function, Cambridge: Cambridge University Press (1922, 1924).
Pearson, K., Tables of the Incomplete Beta Function, Cambridge: Cambridge University Press (1934, 1968).
Peirce, C. S., The probability of induction, Popular Science Monthly, 12 (1878), 705–718 [reprinted in Peirce (1982) and as Arts. 2.669–2.693 of Peirce (1931–1958)].
Peirce, C. S., Collected Papers, Cambridge, MA: Harvard University Press (1931–1958).
Peirce, C. S., Writings of C. S. Peirce: A Chronological Edition. Volume III: 1872–1878, Bloomington, IN: Indiana University Press (1982).
Pietronero, L., Tosatti, E., Tosatti, V., and Vespignani, A., Explaining the uneven distribution of numbers in nature: the laws of Benford and Zipf, Physica A, 293 (2001), 297–304.
Pole, A., West, M., and Harrison, P. J., Applied Bayesian Forecasting and Time Series Analysis, London: Chapman and Hall (1994).
Polson, N., and Tiao, G. C., Bayesian Inference (2 vols) (The International Library of Critical Writings in Econometrics, No. 7), Aldershot: Edward Elgar (1995).
Prentice, R. L., A generalization of the probit and logit model for dose response curves, Biometrika, 32 (1976), 761–768.
Press, S. J., Subjective and Objective Bayesian Statistics: Principles, Models and Applications (2nd edn), New York: John Wiley & Sons (2002) [1st edn published as Bayesian Statistics: Principles, Models and Applications (1989)].
Press, S. J., Applied Multivariate Analysis: Using Bayesian and Frequentist Measures of Inference (2nd edn), Melbourne, FL: Krieger (2009) [1st edn (1982); earlier version published as Applied Multivariate Analysis, New York: Holt, Rinehart, Winston (1972)].
Press, W. H., Teukolsky, S. A., Vetterling, W. T., and Flannery, B. P., Numerical Recipes: The Art of Scientific Computing, Cambridge: Cambridge University Press (1986) [further editions and example books in BASIC, FORTRAN, Pascal and C (1986–1993)].
Price, R., A demonstration of the second rule in the essay towards the solution of a problem in the doctrine of chances, Phil. Trans. Roy. Soc. London, 54 (1764), 296–325.
Raftery, A. E., and Lewis, S. M., Implemeting MCMC, Chapter 7, in Gilks et al. (1996).
Raiffa, H., and Schlaifer, R., Applied Statistical Decision Theory, Cambridge, MA: Harvard University Press (1961).
Raimi, R. A., The first digit problem, Amer. Math. Monthly, 83 (1976), 531–538.
Rauch, B., Göttsche, M., Brähler, G., and Engel, S., Fact and Fiction in EU-Governmental Economic Data, German Economic Review, 12 (2011), 243–255.
Rao, C. R., Linear Statistical Inference and its Applications (2nd edn), New York: John Wiley & Sons (1973) [1st edn (1965)].
Rényi, A., Foundations of Probability, San Francisco, CA: Holden-Day (1970).
Richardson, S., and Green, P. J., On Bayesian analysis of mixtures with an unknown number of components (with discussion), J. Roy. Stat. Soc. Ser. B, 59 (1997), 731–792.
Ripley, B. D., Stochastic Simulation, New York: John Wiley & Sons (1987).
Robert, C., Convergence control methods for Markov chain Monte Carlo algorithms, Statist. Sci., 10 (1995), 231–253.
Robert, C., and Casella, G., Introducing Monte Carlo Methods with R, New York: Springer-Verlag (2010).
Roberts, H. V., Informative stopping rules and inference about population size, J. Amer. Statist. Assoc., 62 (1967), 763–775.
Robinson, G. K., Properties of Student's t and of the Behrens-Fisher solution to the two means problem, Ann. Statist., 4 (1976), 963–971 and 10 (1982), 321.
Rothschild, V., and Logothetis, N., Probability Distributions, New York: John Wiley & Sons (1986).
Rubin, H., Robustness in generalized ridge regression and related topics’, in Bernardo et al. (1988).
Savage, L. J., The Foundations of Statistics, New York: John Wiley & Sons (1954); New York: Dover (1972).
Savage, L. J., The Writings of Leonard Jimmie Savage: A Memorial Selection, Washington, DC: American Statistical Association/Institute of Mathematical Statistics (1981).
Savage, L. J. et al., The Foundations of Statistical Inference: A Discussion, London: Methuen (1962).
Scheffé, H., The Analysis of Variance, New York: John Wiley & Sons (1959).
Schlaifer, R., Introduction to Statistics for Business Decisions, New York: McGraw-Hill (1961).
Schmitt, S. A., Measuring Uncertainty: An Elementary Introduction to Bayesian Statistics, Reading, MA: Addison-Wesley (1969).
Seber, G. A. F., and Lee, A. J., Linear Regression Analysis, New York: John Wiley & Sons (2003) [1st edn (1977)].
Shafer, G., Lindley's paradox, J. Amer. Statist. Assoc., 77 (1982), 325–351.
Shannon, C. E., The mathematical theory of communication, Bell Syst. Tech. J., 27 (1948), 379–423 & 623–656.
Shannon, C. E., and Weaver, W., The mathematical theory of communication, Urbana, IL: University of Illinois Press (1949).
Silcock, A., Verse and Worse, London: Faber & Faber (1952).
Sisson, S. A., and Fan, Y., Likelihood-free Markov chain Monte Carlo, Chapter 12, in Brooks et al. (1980).
Sisson, S. A., Fan, Y., and Tanaka, M. M., Sequential Monte Carlo without likelihoods, Proc. Nat. Acad. Sci., 104 (6) (2007), 1760–1765 and 106 (39) (2009), 16889.
Smith, C. A. B., Biomathematics: The principles of mathematics for students of biological and general science, Volume 2—Numerical Methods, Matrices, Probability, Statistics (4th edn), London: Griffin (1969) (previous one volume edn 1923, 1935, 1954, the first two by W. M. Feldman).
Smith, A. F. M., and Roberts, G. O., Bayesian computation via Gibbs sampler and related Markov Chain Monte Carlo methods, J. Roy. Statist. Soc. B 55 (1993), 3–24.
Spencer, J. E., and Largey, A., Geary on inference in multiple regression and on closeness and the taxi problem, Econ. and Social Rev., 24 (3) (1993), 275–295.
Spiegelhalter, D. J., Best, N. J., Gilks, W. R. and Inskip, H., Hepatitis B: a case study in MCMC methods, Chapter 2, in Gilks et al. (1996).
Sprent, P., Models in Regression and Related Topics, London: Methuen (1969).
Stein, C., Inadmissibility of the usual estimator for the mean of the multivariate normal distribution, Proceedings of the Third Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, CA, and Los Angeles, CA: University of California Press (1956), pp.197–206 [reprinted in Kotz and Johnson (1992–1997, Volume I)].
Stern, C., and Sherwood, E. R. (eds), The Origin of Genetics: A Mendel Source Book, San Francisco: W. H. Freeman (1966).
Stigler, S., American Contributions to Mathematical Statistics in the Nineteenth Century (2 vols), New York: Arno Press (1980).
Stigler, S., The History of Statistics: The Measurement of Uncertainty before 1900, Cambridge, MA: Harvard University Press (1986a).
Stigler, S. M., Statistics on the Table: The History of Statistical Concepts and Methods, Cambridge, MA, and London: Harvard University Press (1999).
Stigler, S., Laplace's 1774 memoir on inverse probability, Statistical Science, 1 (1986b), 359–378.
`Student’ (W. S. Gosset), The probable error of a correlation coefficient, Biometrika, 6 (1908), 1–25.
`Student’ (W. S. Gosset), Student's Collected Papers, Cambridge: Cambridge University Press for Biometrika (1942).
Tanner, M. A., Tools for Statistical Inference: Methods for the Exploration of Posterior Distributions and Likelihood Functions (3rd edn), Berlin: Springer (1996) [1st edn (1991), 2nd edn (1993)].
Tanner, M. A., and Wong, W. H., The calculation of posterior distributions by data augmentation (with discussion), J. Amer. Statist. Assoc.
, 82 (1987), 528–550 [reprinted in Polson and Tiao (1995, Volume II)].
Theobald, C. M., Generalizations of mean square error applied to ridge regression, J. Roy. Statist. Soc. B, 34 (1974), 103–105.
Tierney, L., Markov chains for exploring posterior distributions (with discussion), Annals of Statistics, 22 (1994), 1701–1762.
Todhunter, I., A History of the Mathematical Theory of Probability from the Time of Pascal to That of Laplace, London: Macmillan (1865) [reprinted New York: Chelsea (1949)].
Toni, T., and Stumpf, M. P. H., Simulation-based model selection for dynamical systems in systems and population biology, Bioinformatics, 26 (1) (2010), 104–110.
Turchin, V. F., On the Computation of Multidimensional Integrals by the Monte-Carlo Method, Theory of Probability and its Applications, 16 (4) (1971), 720–724 [translation of K vychisleniyu mnogomernyx integralov po metodu Monte-Carlo, Teoriya Veroyatnostej i ee Primeneniya, 16 (4) (1971), 738–74].
Turner, P. S., The distribution of l.s.d. and its implications for computer design, Math. Gazette, 71 (1987), 26–31.
Venables, W. N., and Ripley, B. D., Modern Applied Statistics with S (4th edn), Berlin: Springer-Verlag 2002 [1st edn (1995), 2nd edn (1997), 3rd edn (1999)].
von Mises, R., Über die `Ganz-zahligkeit’ der Atomgewicht und verwandte Fragen, Physikal. Z., 19 (1918), 490–500.
von Mises, R., On the correct use of Bayes’ formula, Ann. Math. Statist., 13 (1942), 156–165.
von Mises, R., Selected Papers (2 vols), Providence, RI: American Mathematical Society (1963–1964).
von Neumann, J., and Morgenstern, O., Theory of Games and Economic Behaviour, Princeton, NJ: Princeton University Press (1944, 1947, 1953).
Walther, G., Inference and modelling with log-concave distributions, Statistical Science, 24 (2009), 319–327.
Watkins, P., Story of the W and the Z, Cambridge: Cambridge University Press (1986).
Weir, C., and Murray, G., Fraud in clinical trials, Significance, 8 (2011), 164–168.
Weisberg, H. L., Bayesian comparison of two ordered multinomial populations, Biometrics, 23 (1972), 859–867.
Weisberg, S., Applied Linear Regression (3rd edn), New York: John Wiley & Sons (2005) [1st edn (1980), 2nd edn (1985)].
