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The geometric mean has from time to time been used to calculate financial indices (the averaging is over the components of the index). For example, in the past the FT 30 index used a geometric mean. [8] It is also used in the CPI calculation [9] and recently introduced "RPIJ" measure of inflation in the United Kingdom and in the European Union.
the most frequent value in the data set. This is the only central tendency measure that can be used with nominal data, which have purely qualitative category assignments. Generalized mean A generalization of the Pythagorean means, specified by an exponent. Geometric mean the nth root of the product of the data values, where there are n of these ...
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 geometric standard deviation is used as a measure of log-normal dispersion analogously to the geometric mean. [3] As the log-transform of a log-normal distribution results in a normal distribution, we see that the geometric standard deviation is the exponentiated value of the standard deviation of the log-transformed values, i.e. = ( ()).
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.
Two other commonly used F measures are the measure, which weights recall twice as much as precision, and the measure, which weights precision twice as much as recall. The F-measure was derived by van Rijsbergen (1979) so that F β {\\displaystyle F_{\\beta }} "measures the effectiveness of retrieval with respect to a user who attaches β ...
A measure that takes values in the set of self-adjoint projections on a Hilbert space is called a projection-valued measure; these are used in functional analysis for the spectral theorem. When it is necessary to distinguish the usual measures which take non-negative values from generalizations, the term positive measure is used.
The power mean could be generalized further to the generalized f-mean: (, …,) = (= ()) This covers the geometric mean without using a limit with f(x) = log(x). The power mean is obtained for f(x) = x p. Properties of these means are studied in de Carvalho (2016).