R3(References on References on References) on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(1)
R3 on "What are the most important statistical ideas of the past 50 years? " Andrew Gelman, Aki Vehtari(0)
What are the most important statistical ideas of the past 50 years?
Andrew Gelman, Aki Vehtari
References
1
Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In Proceedings of the Second International Symposium on Information Theory, ed. B. N. Petrov and F. Csaki, 267–281. Budapest: Akademiai Kiado. Reprinted in Breakthroughs in Statistics, ed. S. Kotz, 610–624. New York: Springer (1992).
References on 1.
1.1.
Akaike, H., Fitting autoregressive models for prediction. Ann. Inst. Statist. Math. 21 (1969) 243–217.
References on 1.1
T.B.D.
1.2.
Akaike., H., Statistical predictor identification. Ann. Inst. Statist. Math. 22 (1970) 203–217.
References on 1.2
[1]
Akaike H. (1969). Fitting autoregressive models for prediction,Ann. Inst. Statist. Math.,21, 243–247.
https://www.ism.ac.jp/editsec/aism/pdf/021_2_0243.pdf
[2]
Anderson, T. W. (1963). Determination of the order of dependence in normally distributed time series,Time Series Analysis (ed. M. Rosenblatt), New York, John Wiley, 425–446.
https://www.semanticscholar.org/paper/DETERMINATION-OF-THE-ORDER-OF-DEPENDENCE-IN-TIME-Anderson/108ef973f5f2fe99773686f1bdb0be0e9fc1d2c0
[3]
Akaike, H. (1970). On a semi-automatic power spectrum estimation procedure,Proc. 3rd Hawaii International Conference on System Sciences, 974–977.
[4]
Diananda, P. H. (1953). Some probability limit theorems with statistical applications,Proc. Cambridge Philos. Soc.,49, 239–246.
https://www.cambridge.org/core/journals/mathematical-proceedings-of-the-cambridge-philosophical-society/article/abs/some-probability-limit-theorems-with-statistical-applications/3FD6E7D20E03C8FD10B877CD9ADB3B1F
[5]
Anderson, T. W. and Walker, A. M. (1964). On the asymptotic distribution of the autocorrelations of a sample from a linear stochastic process,Ann. Math. Statist.,35, 1296–1303.
https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-35/issue-3/On-the-Asymptotic-Distribution-of-the-Autocorrelations-of-a-Sample/10.1214/aoms/1177703285.full
[6]
Akaike, H. (1969). Power spectrum estimation through autoregressive model fitting,Ann. Inst. Statist. Math.,21, 407–419.
https://link.springer.com/article/10.1007%2FBF02532269
[7]
Mann, H. B. and Wald, A. (1943). On stochastic limit and order relationships,Ann. Math. Statist. 14, 217–226.
https://projecteuclid.org/journals/annals-of-mathematical-statistics/volume-14/issue-3/On-Stochastic-Limit-and-Order-Relationships/10.1214/aoms/1177731415.full
[8]
Durbin, J. (1960). The fitting of time-series models,Rev. Int. Inst. Stat.,28, 233–244.
https://www.jstor.org/stable/1401322?origin=crossref
[9]
Jones, R. H. (1964). Prediction of multivariate time series,J. of Applied Meteorology,3, 285–289.
https://journals.ametsoc.org/view/journals/apme/3/3/1520-0450_1964_003_0285_pomts_2_0_co_2.xml
1.3.
Akaike, H., On a semi-automatic power spectrum estimation procedure. Proc. 3rd Hawaii International Conference on System Sciences, 1970, 974–977.
no URL
References on 1.3
T.B.D.
1.4.
Akaike, H., On a decision procedure for system identification, Preprints, IFAC Kyoto Symposium on System Engineering Approach to Computer Control. 1970, 486–490.
References on 1.4
T.B.D.
1.5.
Akaike, H., Autoregressive model fitting for control. Ann. Inst. Statist. Math. 23 (1971) 163–180.
https://www.ism.ac.jp/editsec/aism/pdf/023_2_0163.pdf
References on 1.5
T.B.D.
1.6.
Akaike, H., Determination of the number of factors by an extended maximum likelihood principle. Research Memo. 44, Inst. Statist. Math. March, 1971.
References on 1.6
T.B.D.
1.7.
Bartlett, M. S., The statistical approach to the analysis of time-series. Symposium on Information Theory (mimeographed Proceedings), Ministry of Supply, London, 1950, 81–101.
References on 1.7
1.M. S. Bartlett, J. Roy. Statist. Soc., no. Suppl. 8, pp. 27, 1946.
2.M. S. Bartlett, Biometrika, vol. 37, no. Suppl. 8, pp. 1, 1950.
3.Ibid Proc.Camb.Phil.Soc. (in Press)
4.M.S. Bartlett and P.H. Diananda, J.R.Statist.Soc..
5.S. Chandrasekhar, Reviews of Modern Physics, vol. 15, pp. 1, 1943.
6.H. Cramer, Mathematical Methods of Statistics, Princeton, 1946.
7.R. A. Fisher, Statistical Methods for Research Workers, Edinburgh, 1925.
8.I. J. Good, Probability and the Weighing of Evidence, London, 1950.
9.U. Grenander, Arkiv for Matematik, vol. 1, no. 17, 1950.
10.M. G. Kendall, Advanced Theory of Statistics, London, vol. 2, 1946.
11.M. G. Kendall, Contributions to the Study of Oscillatory Time Series, Cambridge, 1946.
12.P. Levy, Processus Stochastiques et Mouvement Brownien, Paris, 1948.
13.J.E. Moyal, J.R.Statist.Soc., vol. 11, pp. 150, 1949.
14.M.H. Quenouille, J.R.Statist.Soc., vol. 110, pp. 123, 1947.
15.S. O. Rice, Bell System Techn.J., vol. 23, pp. 282-246, 5 1944.
16.C. Shannon, Bell System Techn.J., vol. 27, pp. 379-623, 1948.
17.N. Wiener, Cybernetics, New York, 1948.
18.N. Wiener, Extrapolation Interpolation and Smoothing of Stationary Time Series, New York, 1949.
19.G. U. Yule, Phil. Trans. A, vol. 226, pp. 267, 1927.
1.8.
Billingsley, P., Statistical Inference for Markov Processes. Univ. Chicago Press, Chicago 1961.
References on 1.8
Bahadur, R. R. (1960) Stochastic comparison of tests. Ann. Math. Statist. 31, 276–295.
Bahadur, R. R. (1967) Rates of convergence of estimates and test statistics. Ann. Math. Statist. 38, 303–324.
Billingsley, P. (1961) Statistical Inference for Markov Processes. The University of Chicago Press, Chicago.
Cramér, H. and Leadbetter, M. R. (1967) Stationary and Related Stochastic Processes. Wiley, New York.
Cramér, H. and Wold, H. (1936) Some theorems on distribution functions. J. London Math. Soc. 11, 290–295.
Doob, J. L. (1953) Stochastic Processes. Wiley, New York.
Foutz, R. V. (1977) On the unique consistent solution to the likelihood equations. J. Amer. Statist. Assoc. 72, 147–148.
Foutz, R. V. and Srivastava, R. C. (1977) The performance of the likelihood ratio test when the model is incorrect. Ann. Statist. 5, 1183–1194.
Foutz, R. V. and Srivastava, R. C. (1978) The asymptotic distribution of the likelihood ratio when the model is incorrect.
Kullback, S. (1959) Information Theory and Statistics. Wiley, New York.
Wald, A. (1949) Note on the consistency of the maximum likelihood estimate. Ann. Math. Statist. 20, 595–601.
1.9.
Blackwell, D., Equivalent comparisons of experiments. Ann. Math. Statist. 24 (1953) 265–272.
References on 1.9
T.B.D.
1.10.
Campbell, L.L., Equivalence of Gauss’s principle and minimum discrimination information estimation of probabilities. Ann. Math. Statist. 41 (1970) 10111015.
References on 1.10
T.B.D.
1.11.
Fisher, R.A., Theory of statistical estimation. Proc. Camb. Phil. Soc. 22 (1925) 700–725, Contributions to Mathematical Statistics John Wiley & Sons, New York, 1950, paper 11.
References on 1.11
1.12.
Good, I.J. Maximum entropy for hypothesis formulation, especially for multidimensional contingency tables. Ann. Math. Statist. 34 (1963) 911–934.
References on 1.12
T.B.D.
1.13.
Gorman, J.W. and Toman, R.J., Selection of variables for fitting equations to data. Technometrics 8 (1966) 27–51.
References on 1.13
1.13.1
The Examination and Analysis of Residuals
F. Anscombe, J. Tukey
Mathematics
1963
A number of methods for examining the residuals remaining after a conventional analysis of variance or least-squares fitting have been explored during the past few years. These give information on… Expand
1.13.2
On multiple regression analysis
H. C. Hamaker
Mathematics
1962
Summary The sums of squares associated with the independent variables in a multiple regression equation depend on the order in which these variables are introduced. Two methods have been proposed in… Expand
1.13.3
Design and analysis of industrial experiments
O. L. Davies, F. En, H. C. Hamaker
Computer Science, Mathematics
1954
This paper is based on a lecture on the “Design and Analysis of Industrial Experiments” given by Dr O. L. Davies on the 8th of May 1954 and the recent designs developed by Box for the exploration of response surfaces are briefly considered. Expand
1.13.4
Use of Half-Normal Plots in Interpreting Factorial Two-Level Experiments
C. Daniel
Mathematics
1959
Plotting the empirical cumulative distribution of the usual set of orthogonal contrasts computed from a 2 p experiment on a special grid may aid in its criticism and interpretation. Bad values,… Expand
1.13.5
On the Experimental Attainment of Optimum Conditions
G. Box, K. B. Wilson
Mathematics
1951
The work described is the result of a study extending over the past few years by a chemist and a statistician. Development has come about mainly in answer to problems of determining optimum… Expand
1.13.6
Mathematical Methods for Digital Computers
A. Ralston, H. Wilf, Peter L. Balise
Computer Science
1960
This is the book that many people in the world waiting for to publish, mathematical methods for digital computers, and the book lovers are really curious to see how this book is actually. Expand
1.13.7
FACTOR SCREENING IN PROCESS DEVELOPMENT
C. Daniel
Chemistry
1963
1.13.8
Statistical Theory and Methodology in Science and Engineering.
W. R. Buckland, K. Brownlee
Engineering, Computer Science
1960
1.13.9
Statistical Theory with Engineering Applications
A. Hald, B. Friedman
Mathematics, Computer Science
1952
1.14.
Jenkins, G.M. and Watts, D.G., Spectral Analysis and Its Applications. Holden Day, San Francisco, 1968.
1.15.
Kullback, S. and Leibler, R.A., On information and sufficiency. Ann. Math Statist. 22 (1951) 79–86.
1.16.
Kullback, S., Information Theory and Statistics. John Wiley & Sons, New York 1959.
1.17.
Le Cam, L., On some asymptotic properties of maximum likelihood estimates and related Bayes estimates. Univ. Calif. Publ. in Stat. 1 (1953) 277–330.
1.18.
Lehmann, E.L., Testing Statistical Hypotheses. John Wiley & Sons, New York 1969.
1.19.
Otomo, T., Nakagawa, T. and Akaike, H. Statistical approach to computer control of cement rotary kilns. 1971. Automatica 8 (1972) 35–48.
1.20.
Rényi, A., Statistics and information theory. Studia Sci. Math. Hung. 2 (1967) 249–256.
1.21.
Savage, L.J., The Foundations of Statistics. John Wiley & Sons, New York 1954.
1.22.
Shannon, C.E. and Weaver, W., The Mathematical Theory of Communication. Univ. of Illinois Press, Urbana 1949.
1.23.
Wald, A., Tests of statistical hypotheses concerning several parameters when the number of observations is large. Trans. Am. Math. Soc. 54 (1943) 426–482.
1.24.
Wald, A., Note on the consistency of the maximum likelihood estimate. Ann Math. Statist. 20 (1949) 595–601.
MathSciNetCrossRefzbMATHGoogle Scholar
1.25.
Wald, A., Statistical Decision Functions. John Wiley & Sons, New York 1950.
1.26.
Whittle, P., The statistical analysis of seiche record. J. Marine Res. 13 (1954) 76–100.
1.27.
Whittle, P., Prediction and Regulation. English Univ. Press, London 1963.
1.28.
Wiener, N., Cybernetics. John Wiley & Sons, New York, 1948.
参考資料(References)
Data Scientist の基礎(2)
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岩波数学辞典 二つの版がCDに入ってお得
https://qiita.com/kaizen_nagoya/items/1210940fe2121423d777
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https://qiita.com/kaizen_nagoya/items/e02cbe23b96d5fb96aa1
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