In these expressions, (U_{ 1i}) is a separate random component for disappearance, W i represents the shared component, and (alpha_{1}) and (alpha_{2}) are specific intercepts for disappearance and homicide, respectively.
Fig. 4 Posterior median relative risks for disappearance and homicide in El Salvador from the shared component model.
SC creates two distinct species of the shared component and therefore the derivatives with respect to Hill Exponents change to: (7.3) ∂ S X ∂ n > 0 ⇔ x 1 N | X i n > ε X ∂ S Y ∂ n > 0 ⇔ x 1 N | Y i n < ε X ∂ S X ∂ m > 0 ⇔ x 1 C | X i n < ε Y ∂ S Y ∂ m > 0 ⇔ x 1 C | Y i n > ε Y Combining these new equations gives: (7.4) x 1 N | Y i n < ε X < x 1 N | X i n, x 1 C | X i n < ε Y < x 1 C | Y i n.
In this case, the macro-cell edge user can receive data from its associated macro-cell on the macro-cell component carriers and the closest small-cell on the shared component carriers that are used by macro-cells and small cells, simultaneously [4,5].
We start by examining the case where a signal modulates the production of the shared component, X, and continue by examining the case where signals modulate the components A and B, both separately and simultaneously.
This is achieved assuming log(μij) = log eij) + αj + δjφi + ϵij, where αj values are the intercepts for each j-th PPH condition, ϵij are the corresponding specific effects, and φi the random effect representing the shared component of the risk.
Figure 5 displays relative risk associated with the shared component.
The shared component (term σShared = exp φi)) is mapped in Figure 1, together with the map of the posterior probability that this shared component was above 1.
Each of the pathways of necessity interacted with the other, because of the shared component.
Hence, most of the risk was partitioned into the shared component, suggesting a weak residual signal.
