In mathematics and statistics, the quasi-arithmetic mean or generalised f-mean or Kolmogorov-Nagumo-de Finetti mean[1] is one generalisation of the more familiar means such as the arithmetic mean and the geometric mean, using a function . It is also called Kolmogorov mean after Soviet mathematician Andrey Kolmogorov. It is a broader generalization than the regular generalized mean.
Definition
editIf f is a function which maps an interval of the real line to the real numbers, and is both continuous and injective, the f-mean of numbers is defined as , which can also be written
We require f to be injective in order for the inverse function to exist. Since is defined over an interval, lies within the domain of .
Since f is injective and continuous, it follows that f is a strictly monotonic function, and therefore that the f-mean is neither larger than the largest number of the tuple nor smaller than the smallest number in .
Examples
edit- If , the real line, and , (or indeed any linear function , not equal to 0) then the f-mean corresponds to the arithmetic mean.
- If , the positive real numbers and , then the f-mean corresponds to the geometric mean. According to the f-mean properties, the result does not depend on the base of the logarithm as long as it is positive and not 1.
- If and , then the f-mean corresponds to the harmonic mean.
- If and , then the f-mean corresponds to the power mean with exponent .
- If and , then the f-mean is the mean in the log semiring, which is a constant shifted version of the LogSumExp (LSE) function (which is the logarithmic sum), . The corresponds to dividing by n, since logarithmic division is linear subtraction. The LogSumExp function is a smooth maximum: a smooth approximation to the maximum function.
Properties
editThe following properties hold for for any single function :
Symmetry: The value of is unchanged if its arguments are permuted.
Idempotency: for all x, .
Monotonicity: is monotonic in each of its arguments (since is monotonic).
Continuity: is continuous in each of its arguments (since is continuous).
Replacement: Subsets of elements can be averaged a priori, without altering the mean, given that the multiplicity of elements is maintained. With it holds:
Partitioning: The computation of the mean can be split into computations of equal sized sub-blocks:
Self-distributivity: For any quasi-arithmetic mean of two variables: .
Mediality: For any quasi-arithmetic mean of two variables: .
Balancing: For any quasi-arithmetic mean of two variables: .
Central limit theorem : Under regularity conditions, for a sufficiently large sample, is approximately normal.[2] A similar result is available for Bajraktarević means and deviation means, which are generalizations of quasi-arithmetic means.[3][4]
Scale-invariance: The quasi-arithmetic mean is invariant with respect to offsets and scaling of : .
Characterization
editThere are several different sets of properties that characterize the quasi-arithmetic mean (i.e., each function that satisfies these properties is an f-mean for some function f).
- Mediality is essentially sufficient to characterize quasi-arithmetic means.[5]: chapter 17
- Self-distributivity is essentially sufficient to characterize quasi-arithmetic means.[5]: chapter 17
- Replacement: Kolmogorov proved that the five properties of symmetry, fixed-point, monotonicity, continuity, and replacement fully characterize the quasi-arithmetic means.[6]
- Continuity is superfluous in the characterization of two variables quasi-arithmetic means. See [10] for the details.
- Balancing: An interesting problem is whether this condition (together with symmetry, fixed-point, monotonicity and continuity properties) implies that the mean is quasi-arithmetic. Georg Aumann showed in the 1930s that the answer is no in general,[7] but that if one additionally assumes to be an analytic function then the answer is positive.[8]
Homogeneity
editMeans are usually homogeneous, but for most functions , the f-mean is not. Indeed, the only homogeneous quasi-arithmetic means are the power means (including the geometric mean); see Hardy–Littlewood–Pólya, page 68.
The homogeneity property can be achieved by normalizing the input values by some (homogeneous) mean .
However this modification may violate monotonicity and the partitioning property of the mean.
Generalizations
editConsider a Legendre-type strictly convex function . Then the gradient map is globally invertible and the weighted multivariate quasi-arithmetic mean[9] is defined by , where is a normalized weight vector ( by default for a balanced average). From the convex duality, we get a dual quasi-arithmetic mean associated to the quasi-arithmetic mean . For example, take for a symmetric positive-definite matrix. The pair of matrix quasi-arithmetic means yields the matrix harmonic mean:
See also
editReferences
edit- Andrey Kolmogorov (1930) "On the Notion of Mean", in "Mathematics and Mechanics" (Kluwer 1991) — pp. 144–146.
- Andrey Kolmogorov (1930) Sur la notion de la moyenne. Atti Accad. Naz. Lincei 12, pp. 388–391.
- John Bibby (1974) "Axiomatisations of the average and a further generalisation of monotonic sequences," Glasgow Mathematical Journal, vol. 15, pp. 63–65.
- Hardy, G. H.; Littlewood, J. E.; Pólya, G. (1952) Inequalities. 2nd ed. Cambridge Univ. Press, Cambridge, 1952.
- B. De Finetti, "Sul concetto di media", vol. 3, p. 36996, 1931, istituto italiano degli attuari.
- ^ Nielsen, Frank; Nock, Richard (June 2017). "Generalizing skew Jensen divergences and Bregman divergences with comparative convexity". IEEE Signal Processing Letters. 24 (8): 2. arXiv:1702.04877. Bibcode:2017ISPL...24.1123N. doi:10.1109/LSP.2017.2712195. S2CID 31899023.
- ^ de Carvalho, Miguel (2016). "Mean, what do you Mean?". The American Statistician. 70 (3): 764‒776. doi:10.1080/00031305.2016.1148632. hdl:20.500.11820/fd7a8991-69a4-4fe5-876f-abcd2957a88c. S2CID 219595024.
- ^ Barczy, Mátyás; Burai, Pál (2022-04-01). "Limit theorems for Bajraktarević and Cauchy quotient means of independent identically distributed random variables". Aequationes Mathematicae. 96 (2): 279–305. doi:10.1007/s00010-021-00813-x. ISSN 1420-8903.
- ^ Barczy, Mátyás; Páles, Zsolt (2023-09-01). "Limit Theorems for Deviation Means of Independent and Identically Distributed Random Variables". Journal of Theoretical Probability. 36 (3): 1626–1666. doi:10.1007/s10959-022-01225-6. ISSN 1572-9230.
- ^ a b Aczél, J.; Dhombres, J. G. (1989). Functional equations in several variables. With applications to mathematics, information theory and to the natural and social sciences. Encyclopedia of Mathematics and its Applications, 31. Cambridge: Cambridge Univ. Press.
- ^ Grudkin, Anton (2019). "Characterization of the quasi-arithmetic mean". Math stackexchange.
- ^ Aumann, Georg (1937). "Vollkommene Funktionalmittel und gewisse Kegelschnitteigenschaften". Journal für die reine und angewandte Mathematik. 1937 (176): 49–55. doi:10.1515/crll.1937.176.49. S2CID 115392661.
- ^ Aumann, Georg (1934). "Grundlegung der Theorie der analytischen Analytische Mittelwerte". Sitzungsberichte der Bayerischen Akademie der Wissenschaften: 45–81.
- ^ Nielsen, Frank (2023). "Beyond scalar quasi-arithmetic means: Quasi-arithmetic averages and quasi-arithmetic mixtures in information geometry". arXiv:2301.10980 [cs.IT].
[10] MR4355191 - Characterization of quasi-arithmetic means without regularity condition
Burai, P.; Kiss, G.; Szokol, P. Acta Math. Hungar. 165 (2021), no. 2, 474–485.
[11]
MR4574540 - A dichotomy result for strictly increasing bisymmetric maps
Burai, Pál; Kiss, Gergely; Szokol, Patricia
J. Math. Anal. Appl. 526 (2023), no. 2, Paper No. 127269, 9 pp.