El estado de la soberanía alimentaria en Ecuador

Model (3.3) has been left intentionally generic, so that any copula function is in principle allowed to bind together the marginal distributions included in the model representation. However, the proposed implementation is practically restricted to the case of Standard Nor- mal marginals and to the class of Archimedean copulae. This set up has some appealing features which makes its implementation particularly attractive. Specically, for any given marginal distribution, Archimedean copulae are dened as the associative class of functions with generator ψ : [0, 1] −→ [0, +∞) which is assumed to be continuous, convex, decreasing, d-monotone, dierentiable and such that ψ(1) = 0 (McNeil and Ne²lehová, 2009); namely

ψ(Cγ(F1,1, F1,2)) = ψ(F1,1) + ψ(F1,2) = ψ(Φ(η1,k1)) + ψ(Φ(η2,k2)).

At this stage of the research, the marginals have been set to the Standard Normal cdf to ease the comparison with the relevant applied literature in which this distribution is widely used (see also Chapter 2). Nonetheless, representation (3.3) would in principle allow us to employ dierent marginals since the theoretical (and computational) framework remain essentially unchanged. Table 3.1 lists the various copula functions implemented in this work, whereas a graphical representation of their contours is provided in Appendix B.2. For completeness, we also consider rotated versions of the Clayton, Gumbel and Joe, which allow us to model negative dependences (for the 90 and 270 degrees), otherwise not implied by the respective canonical denitions. Analytically, rotations are computed using the denitions of Brechmann and Schepsmeier (2013) as reported in Appendix B.2; an example from the Joe copula is plotted in Figure 3.1. Two possible shortcomings may emerge from the above

3.2. Methods 53

Name Cγ(u, v) Support of γ γ∗

Gaussian Φ2(Φ−1(u), Φ−1(v)) [−1, 1] tanh−1(γ)

Clayton (u−γ+ v−γ− 1)−1/γ _{(0, ∞) log(γ − ε)}

Frank −γ−1_{log[1 + (e}−γu_{− 1)(e}−γv_{− 1)/(e}−γ_{− 1)]}

R \ {0} γ − ε

Gumbel exp−[(− log u) + (− log v)]1/γ [1, ∞) log(γ − 1)

Joe 1 − [(1 − u)γ_{+ (1 − v)}γ_{− (1 − u)}γ_{(1 − v)}γ_]1/γ _{(1, ∞) log(γ − 1 − ε)}

Table 3.1: Families of some bivariate copula functions with association parameter γ. For optimisation purposes, an appropriate transformation γ∗_{, given in the last column of the}

table, is used in the estimation algorithm. The quantity ε denotes the machine smallest oating point multiplied by 106_{, and is introduced to force the transformed association}

parameters to lie in their respective supports throughout estimation. Finally, we have dened uand v to denote the marginals Φ(ηj,kj)for j = 1, 2.

specication of bivariate Archimedean copulae. A rst one relates to their characterisation through a unique association parameter γ. Although restrictive, a possible alleviation can be achieved by dening a suitable transformation of γ (namely γ∗ _{as in Table 3.1) in terms of a}

linear predictor too. This is eectively equivalent to assume a dierent association parameter for every individual in the sample. A further limitation is that the copulae employed in this chapter are exchangeable (Durante, 2009; Frees and Valdez, 1998). In the context of two- car accidents, for example, exchangeability implies that the probability of the two drivers to sustain a certain level of injury severities is invariant to weather (Y1 = k1, Y2 = k2)

or (Y2 = k2, Y1 = k1) because they both give rise to the same bivariate distribution. For

a complete account of copulae and their theoretical properties we refer the reader to the monograph of Nelsen (2006), whereas an excellent practical guide to copula modelling is oered by Trivedi and Zimmer (2005).

From an interpretational standpoint, equation (3.3) explicates that the cumulative mass function of the random vector Y is linked to the cdf of a latent (unobserved) random vector Y∗ := (Y₁∗, Y₂∗)>, with support the extended real plane, through the equivalence

{Y  k} ⇐⇒ {ε ≤ η_k},

where ε := (ε1, ε2)> denotes the stochastic component in the regression of Y∗ onto the

columns of X. In our context, we assume that each crash conguration carries a certain propensity to injury severities as described by the variables in X and the random components ε. This propensity is then monotonically translated into an observed injury severity level sustained by the individuals involved in the accident. The construct above is standard in ordinal response modelling and traces back at least to the work of McKelvey and Zavoina (1975), with an explicit reference also in McCullagh (1980). The probability of the event

3.2. Methods 54 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 0 degrees u v 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 90 degrees u v 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 180 degrees u v 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 270 degrees u v

Figure 3.1: Random samples of 1,000 observations obtained from the two-place Joe copula with both Standard Normal marginals and dierent degrees of rotation. The association parameter has been xed such that it corresponds to a Kendall's τ of 0.5 (−0.5 in case of 90and 270 degrees). Explicit correspondences between γ and Kendall's τ in the context of Archimedean copulae are standard, and can be found in Brechmann and Schepsmeier (2013), for example.

{Y = k}, namely π_k, is nally recovered by inverting the right-hand side of (3.3) which results in πk= r−1(r(πk)) =Pl,m∈{0,1}(−1)l+mr(πk1−l,k2−m).

3.2.2 Penalized Regression Splines Approximation

Dierent covariate types are included in the proposed model. In particular, for any con- tinuous regressor vj,lj ∈ R, like drivers' age or time of the accident (as expressed in hours

and minutes), we advocate a non-parametric approach to curve estimation using penalized regression splines. That is, let their functional forms be smooth curves, sj,lj : R −→ R, then

we seek to represent such covariate eects without the imposition of any given parametric structure. Let us assume rst that we observe a sample of size n, {xi, yi}i. Then, by appro-

priately choosing Hj+ 1, Hj := H(j, lj) < n, knot points in the interior of [vj,lj,(1),vj,lj,(n)],

with vj,lj,(i) ≤ vj,lj,(i+1) for any i, it is possible to approximate the generic sj,lj-th curve

as a linear combination of known basis spline functions, bj,lj, and corresponding unknown

coecients, δj,lj, to be estimated alongside the other model components. In other words we

set

sj,lj(vj,lj,i) ≈ δ>j,ljbj,lj(vj,lj,i),

where the above vectors are Hj-dimensional. Moreover, since the curve estimates are only

identied up to an intercept term, a centering constraint of the form 1>

nsj,lj = 0, with sj,lj

being the vector whose i-th element is sj,lj(vj,lj,i), has to be imposed. This is achieved by

employing the parsimonious method proposed by Wood (2006).

Basis functions are usually chosen based on their mathematical tractability and nu- merical stability. Among the most widely used in applications, we mention the cubic, pe-

3.2. Methods 55 nalized B-splines (Eilers and Marx, 1996) and thin plate regression splines (Wood, 2003), which are all supported by the computational routine attached to this chapter. Notice that the bases can be included in the design matrix by specifying the arrays X[j,lj] :=

(bj,lj(vj,lj,1)| · · · |bj,lj(vj,lj,n))> ∈ Rn×Hj and, accordingly, β[j,lj] := δj,lj ∈ RHj. Thus it

holds that

η_j = cj − Xj,1βj,1− · · · − Xj,Mjβj,Mj = Zjβj ∈ Rn

is the linear predictor corresponding to the j-th equation, with Zj := (Ij, −Xj,1, . . . , −Xj,Mj)

and βj := vec(cj,kj, βj,1, . . . , βj,Mj), where Ij := diag(1yj,i=kj)i,kj ∈ {0, 1}n×Kj−1 and

cj,kj := (cj,kj)kj ∈ RKj−1. The above representation is pivotal in applied modelling as it

includes both non- and purely parametric covariate eects. In the statistical literature this form is commonly termed semi-parametric (e.g. Ruppert et al., 2003, Wood, 2006) and, once qualied with equation (3.2), the additive extension of a CLM emerges. We consequently label it a Cumulative Link Additive Model, which reads as

r(π) = C2(Φ(Z1β1), Φ(Z2β2); γ) ∈ [0, 1]n,

where π := (π1, . . . , πn)>[0, 1]n and πi:= P[y1,i = k1,y2,i = k2]for i = 1, . . . , n.