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The equally distributed welfare equivalent income associated with an Atkinson Index with an inequality aversion parameter of 1.0 is simply the geometric mean of incomes. For values other than one, the equivalent value is an Lp norm divided by the number of elements, with p equal to one minus the inequality aversion parameter.
It repeatedly replaces two numbers by their arithmetic and geometric mean, in order to approximate their arithmetic-geometric mean. The version presented below is also known as the Gauss–Euler, Brent–Salamin (or Salamin–Brent) algorithm; [1] it was independently discovered in 1975 by Richard Brent and Eugene Salamin.
The arithmetic mean, or less precisely the average, of a list of n numbers x 1, x 2, . . . , x n is the sum of the numbers divided by n: + + +. The geometric mean is similar, except that it is only defined for a list of nonnegative real numbers, and uses multiplication and a root in place of addition and division:
The Törnqvist or Törnqvist-Theil index is the geometric average of the n price relatives of the current to base period prices (for n goods) weighted by the arithmetic average of the value shares for the two periods. [16] [17]
In mathematics, the arithmetic–geometric mean (AGM or agM [1]) of two positive real numbers x and y is the mutual limit of a sequence of arithmetic means and a sequence of geometric means. The arithmetic–geometric mean is used in fast algorithms for exponential , trigonometric functions , and other special functions , as well as some ...
Nomograms to graphically calculate arithmetic (1), geometric (2) and harmonic (3) means, z of x=40 and y=10 (red), and x=45 and y=5 (blue) Of all pairs of different natural numbers of the form ( a , b ) such that a < b , the smallest (as defined by least value of a + b ) for which the arithmetic, geometric and harmonic means are all also ...
This family includes the logarithmic mean, the geometric mean, the identric mean and the arithmetic mean. The Stolarsky means can be justified as minimizing these misrepresentation metrics, which are of major importance in the study of information theory .
Then the maximum spacing estimator of θ 0 is defined as a value that maximizes the logarithm of the geometric mean of sample spacings: ^ = (), = + + = + = + (). By the inequality of arithmetic and geometric means , function S n ( θ ) is bounded from above by −ln( n +1), and thus the maximum has to exist at least in the supremum sense.