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and −2 is the least absolute remainder. In the division of 42 by 5, we have: 42 = 8 × 5 + 2, and since 2 < 5/2, 2 is both the least positive remainder and the least absolute remainder. In these examples, the (negative) least absolute remainder is obtained from the least positive remainder by subtracting 5, which is d. This holds in general.
Least absolute deviations (LAD), also known as least absolute errors (LAE), least absolute residuals (LAR), or least absolute values (LAV), is a statistical optimality criterion and a statistical optimization technique based on minimizing the sum of absolute deviations (also sum of absolute residuals or sum of absolute errors) or the L 1 norm of such values.
In arithmetic, Euclidean division – or division with remainder – is the process of dividing one integer (the dividend) by another (the divisor), in a way that produces an integer quotient and a natural number remainder strictly smaller than the absolute value of the divisor. A fundamental property is that the quotient and the remainder ...
The theorem which underlies the definition of the Euclidean division ensures that such a quotient and remainder always exist and are unique. [20] In Euclid's original version of the algorithm, the quotient and remainder are found by repeated subtraction; that is, r k−1 is subtracted from r k−2 repeatedly until the remainder r k is smaller ...
Pages for logged out editors learn more. Contributions; Talk; Least absolute deviation
This solution has been rediscovered in different disciplines and is variously known as standardised major axis (Ricker 1975, Warton et al., 2006), [14] [15] the reduced major axis, the geometric mean functional relationship (Draper and Smith, 1998), [16] least products regression, diagonal regression, line of organic correlation, and the least ...
The maximum absolute deviation around an arbitrary point is the maximum of the absolute deviations of a sample from that point. While not strictly a measure of central tendency, the maximum absolute deviation can be found using the formula for the average absolute deviation as above with m ( X ) = max ( X ) {\displaystyle m(X)=\max(X)} , where ...
The upper bound is called sharp if equality holds for at least one value of x. It indicates that the constraint is optimal, and thus cannot be further reduced without invalidating the inequality. Similarly, a function g defined on domain D and having the same codomain (K, ≤) is an upper bound of f, if g(x) ≥ f (x) for each x in D.