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In decision theory, the weighted sum model (WSM), [1] [2] also called weighted linear combination (WLC) [3] or simple additive weighting (SAW), [4] is the best known and simplest multi-criteria decision analysis (MCDA) / multi-criteria decision making method for evaluating a number of alternatives in terms of a number of decision criteria.
Fundamentally based indexes or fundamental indexes, also called fundamentally weighted indexes, are indexes in which stocks are weighted according to factors related to their fundamentals such as earnings, dividends and assets, commonly used when performing corporate valuations. This fundamental weight may be calculated statically, or it may be ...
Canonical factor analysis, also called Rao's canonical factoring, is a different method of computing the same model as PCA, which uses the principal axis method. Canonical factor analysis seeks factors that have the highest canonical correlation with the observed variables. Canonical factor analysis is unaffected by arbitrary rescaling of the data.
A common version of capitalization weighting is the free-float weighting. With this method a float factor is assigned to each stock to account for the proportion of outstanding shares that are held by the general public, as opposed to "closely held" shares owned by the government, royalty, or company insiders (see float).
Point factor analysis (PFA) is a systemic bureaucratic method for determining a relative score for a job. [1] Jobs can then be banded into grades, and the grades used to determine pay. [2] PFA is a type of job evaluation; the main advantage of PFA is that it is systemic and analytical. Jobs are broken down into factors such as “knowledge ...
Weighted least squares (WLS), also known as weighted linear regression, [1] [2] is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance of observations (heteroscedasticity) is incorporated into the regression.
The weighted mean in this case is: ¯ = ¯ (=), (where the order of the matrix–vector product is not commutative), in terms of the covariance of the weighted mean: ¯ = (=), For example, consider the weighted mean of the point [1 0] with high variance in the second component and [0 1] with high variance in the first component.
Another use of weighting is in television, in which the red, green and blue components of the signal are weighted according to their perceived brightness. This ensures compatibility with black and white receivers and also benefits noise performance and allows separation into meaningful luminance and chrominance signals for transmission.