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Tauberian theory for the asymptotic forms of statistical frequency functions

Published online by Cambridge University Press:  24 October 2008

J. M. Hammersley
Affiliation:
Lectureship in the Design and Analysis of Scientific ExperimentUniversity of Oxford

Extract

An Abelian theorem is a theorem stating that a given behaviour on the part of each of several quantities entails similar behaviour for their average. A Tauberian theorem is a converse to an Abelian theorem. As a rule, a given behaviour of an average will not entail similar behaviour of the individual quantities themselves unless there is some condition imposed to secure reasonably uniform behaviour amongst the individuals. Such a condition, known as a Tauberian condition, is usually sufficient but not necessary, and it enters into the premises of the Tauberian theorem. We interpret ‘average’ in a wide sense to include any kind of smoothing process; for example, the integral of a function f(t) is an average of the values of f(t) corresponding to individual values of t; and we may seek a sufficient Tauberian condition such that a limit-behaviour of an integral-average

entails the corresponding limit-behaviour

for individual values of t.

Type
Research Article
Copyright
Copyright © Cambridge Philosophical Society 1952

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References

REFERENCES

(1)Blumberg, H.On convex functions. Trans. Amer. math. Soc. 20 (1919), 40–4.CrossRefGoogle Scholar
(2)Cramér, H.Mathematical methods of statistics (Princeton, 1946).Google Scholar
(3)Dantzig, G. B.On a class of distributions that approach the normal distribution function. Ann. math. Statist. 10 (1939), 247–53.CrossRefGoogle Scholar
(4)Haden, H. G.A note on the distribution of the different orderings of n objects. Proc. Camb. phil. Soc. 43 (1947), 19.CrossRefGoogle Scholar
(5)Hammersley, J. M.On estimating restricted parameters. J. R. statist. Soc. B, 12 (1950), 192240.Google Scholar
(6)Hammersley, J. M.The distribution of distance in a hypersphere. Ann. math. Statist. 21 (1950), 447–52.CrossRefGoogle Scholar
(7)Hammersley, J. M.The sums of products of the natural numbers. Proc. Lond. math. Soc. (3), 1 (1951), 435–52.CrossRefGoogle Scholar
(8)Kendall, M. G.Advanced theory of statistics, vol. 1 (London, 1943).Google Scholar
(9)Kendall, M. G.Rank correlation methods (London, 1948).Google Scholar
(10)Moran, P. A. P.Rank correlation and permutation distributions. Proc. Camb. phil. Soc. 44 (1948), 142–4.CrossRefGoogle Scholar