We first derive the marginal distribution of W i with realization w i for i=1,…,m.
The copula theory does not specify the way to derive the marginal distributions and does not lead to a unique copula.
Based on the common structure, we then derive the marginal distributions of the ordered eigenvalues, using a classical result from the theory of order statistics, along with the multilinear property of the determinant.
After estimation, we use the linear model with coefficient estimates to derive the marginal effects of unit changes in the managed care or poverty variables in each of the regulatory regimes, and report these at the bottom of the tablesc.
Optimizing (3) subject to (4) and the equilibrium condition provided at lemma 2.2, we can derive the marginal rate of substitution for a risk-averse entrepreneur across the two states of production.
On the basis of Equation (19), it is straightforward to derive the marginal PDF of R i (i = 1, 2,..., n), given the presence of n points in the interval I : f R i ( n ) ( r i ) = n ! z n ( z - r i ) ( n - i ) r i i - 1 ( n - 1 ) ! ( i - 1 ) ! r i ∈ ( 0, z ) i = 1, …, n 0 otherwise.
The average values can be computed by firstly deriving the joint PDF of the vector R(n), denoted as f R ( n ) ( r ), and defined over a proper n-dimensional domain D n. From f R ( n ) ( r ), it is then possible to derive the marginal PDF of R j (j = 1, 2,..., n), denoted as f R j ( n ) ( r j ) and, from this, the average value R ¯ j ( n ).
This involves learning the parameters of the network model and considering all possible combination of parameterization of the model to derive the marginal probability p(D| M).
Then, this ratio was multiplied by the cost per CHW visit to derive the marginal cost per woman for CHW home visits.
With the inbreeding coefficients calculated, we can derive the marginal probability of observing transition in one of the I k haplotypes, P1 T = P(S(a l )≠ S(a l +1))= P(S(b l )≠ S(b l +1)).
