Sentence examples for marginal sums from inspiring English sources
To decompose the space of an IO table into the scale of the row/column sums (hereafter referred to as the marginal sums) and the space of an industrial structure, we introduce dual and mixed coordinates and perform orthogonal decomposition.
This approach will be termed normal marginals, although, of course, the row marginal sums r 1…r n are discrete by nature; it is the underlying parameters that are sampled from the normal.
Although the orthogonal decomposition of the IO tables has so far been considered using a marginal sum and kernel structure matrix, IO tables can also be reconstructed by combining various marginal sums and kernel structure matrices.
Formally, we are interested in computing the sum: where Γ represents the set of all 2 × k contingency tables with the same marginal sums as T and P T) for any table T is defined by Equation 1.
The calculated p-value, which measures the significance of deviation from the null hypothesis that the gene is not differentially expressed, is computed exactly by using the hypergeometric probability of observed or more extreme differences while keeping the marginal sums in the contingency table unchanged.
For tables with large k and A, an alternate way to compute the P-value is by simulating many random contingency tables with the same marginal sums as T and counting the number of tables T��� with probability P(T′)≤ P T).
For the first (2n-1) vectors, ({varvec{a}}^prime {varvec{b}}_i) corresponds to the marginal sum.
end{aligned} (20)The terms on the right-hand side correspond to the marginal sum and structure.
end{aligned} (22 Let (V_r) be the space generated by changing only the marginal sum by the RAS method.
Regarding the basis of the orthogonal decomposition, the RAS method changes only the marginal sum and the amount representing the structure therefore corresponds to the RAS invariants.
Reconstruction goal is make a matrix ({varvec{a}}_0) as (({varvec{eta }}_0^A,{varvec{theta }}^B_0)) denoted by the mixed coordinates, whose kernel structure matrix is ({mathcal {K}}_{mathrm{any}}) and whose marginal sum is ({varvec{eta}}^A_{mathrm{any}}) corresponding to (AV times {varvec{FL}}) and (AV times {varvec{BL}}) in SDA formulation.
