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The MADi component corresponds then to i-th difference bearing the maximum variance between two sets of variables that are positively correlated.
For all investigated markers (except D-loop and G3PDH) the three-population-model showed the maximum variance between groups and the lowest among populations within each group (Table 2).
It determines the linear combinations of sensor values so that the maximum variance between all data points can be obtained in mutually orthogonal dimensions.
Principal component analysis determines the linear combinations of the input values such that the maximum variance between all data points can be obtained in mutually orthogonal dimensions.
To obtain frailty index strata with minimum variance within observations in a particular stratum and maximum variance between strata, five frailty index levels were determined by a minimum variance method proposed by Dalenius [ 20].
Instead of finding hyperplane of maximum variance between the input and response variables, it finds a linear regression model by projecting both variables to a new space (Boulesteix and Strimmer, 2007; Fornell and Bookstein, 1982; Lê Cao et al., 2008; Liu and Rayens, 2007; Tenenhaus et al., 2005).
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In this case, the maximum variance criterion should be used.
The new axes lie along the directions of maximum variance.
To do so, PCA considers the maximum variance in the dataset, whereas MAD considers maximum autocorrelation, since it takes into account the maximum variance of the difference images.
The first PCA score attains the maximum variance among the linear combination of the three scales.
The linear combination that extracts the maximum variance from the data is termed the principal component.
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