tsicEmp {satdad} | R Documentation |
Empirical tail superset importance coefficients.
Description
Computes on a sample the tail superset importance coefficients (tsic) associated with threshold k
. The value may be renormalized by the empirical global variance (Sobol version) and/or by its theoretical upper bound.
Usage
tsicEmp(sample, ind = 2, k, sobol = FALSE, norm = FALSE)
Arguments
sample |
A |
ind |
A character string among "with.singletons" and "all" (without singletons), or an integer in |
k |
An integer smaller or equal to |
sobol |
A boolean. 'FALSE' (the default). If 'TRUE': the index is normalized by the empirical global variance. |
norm |
A boolean. 'FALSE' (the default). If 'TRUE': the index is normalized by its theoretical upper bound. |
Details
The theoretical functional decomposition of the variance of the stdf \ell
consists in writing D(\ell) = \sum_{I \subseteq \{1,...,d\}} D_I(\ell)
where D_I(\ell)
measures the variance of \ell_I(U_I)
the term associated with subset I
in the Hoeffding-Sobol decomposition of \ell
; note that U_I
represents a random vector with independent standard uniform entries.
Fixing a subset of components I
, the theoretical tail superset importance coefficient is defined by \Upsilon_I(\ell)=\sum_{J \supseteq I} D_J(\ell)
.
A theoretical upper bound for tsic \Upsilon_I(\ell)
is given by Theorem 2 in Mercadier and Ressel (2021)
which states that \Upsilon_I(\ell)\leq 2(|I|!)^2/((2|I|+2)!)
.
Here, the function tsicEmp
evaluates, on a n
-sample and threshold k
, the empirical tail superset importance coefficient \hat{\Upsilon}_{I,k,n}
the empirical counterpart of \Upsilon_I(\ell)
.
Under the option sobol = TRUE
, the function tsicEmp
returns \dfrac{\hat{\Upsilon}_{I,k,n}}{\hat{D}_{k,n}}
the empirical counterpart of \dfrac{\Upsilon_I(\ell)}{D_I(\ell)}
.
Under the option norm = TRUE
, the quantities are multiplied by \dfrac{(2|I|+2)!}{2(|I|!)^2}
.
Proposition 1 and Theorem 2 of Mercadier and Roustant (2019) provide several rank-based expressions
\hat{\Upsilon}_{I,k,n}=\frac{1}{k^2}\sum_{s=1}^n\sum_{s^\prime=1}^n \prod_{t\in I}(\min(\overline{R}^{(t)}_s,\overline{R}^{(t)}_{s^\prime})-\overline{R}^{(t)}_{s}\overline{R}^{(t)}_{s^\prime}) \prod_{t\notin I} \min(\overline{R}^{(t)}_s,\overline{R}^{(t)}_{s^\prime})
\hat{D}_{k,n}=\frac{1}{k^2}\sum_{s=1}^n\sum_{s^\prime=1}^n \prod_{t\in I}\min(\overline{R}^{(t)}_s,\overline{R}^{(t)}_{s^\prime})- \prod_{t\in I}\overline{R}^{(t)}_{s}\overline{R}^{(t)}_{s^\prime}
where
-
k
is the threshold parameter, -
n
is the sample size, -
X_1,...,X_n
describes thesample
, eachX_s
is a d-dimensional vectorX_s^{(t)}
fort=1,...,d
, -
R^{(t)}_s
denotes the rank ofX^{(t)}_s
amongX^{(t)}_1, ..., X^{(t)}_n
, and
\overline{R}^{(t)}_s = \min((n- R^{(t)}_s+1)/k,1)
.
Value
The function returns a list of two elements:
subsets
A list of subsets from\{1,...,d\}
.When
ind
is given as an integer,subsets
is the list of subsets from\{1,...,d\}
with cardinalityind
. Whenind
is the list, it corresponds tosubsets
.When
ind = "with.singletons"
subsets is the list of all non empty subsets in\{1,...,d\}
.When
ind = "all"
subsets is the list of all subsets in\{1,...,d\}
with cardinality larger or equal to 2.tsic
A vector of empirical tail superset importance coefficients associated with the listsubsets
. Whennorm = TRUE
, then tsic are normalized in the sense that the original values are divided by corresponding upper bounds.
Author(s)
Cécile Mercadier (mercadier@math.univ-lyon1.fr
)
References
Mercadier, C. and Ressel, P. (2021) Hoeffding–Sobol decomposition of homogeneous co-survival functions: from Choquet representation to extreme value theory application. Dependence Modeling, 9(1), 179–198.
Mercadier, C. and Roustant, O. (2019) The tail dependograph. Extremes, 22, 343–372.
See Also
Examples
## Fix a 6-dimensional asymmetric tail dependence structure
ds <- gen.ds(d = 6, sub = list(1:4,5:6))
## Plot the tail dependograph
graphs(ds)
## Generate a 1000-sample of Archimax Mevlog random vectors
## associated with ds and underlying distribution exp
sample <- rArchimaxMevlog(n = 1000, ds = ds, dist = "exp", dist.param = 1.3)
## Compute tsic values associated with subsets
## of cardinality 2 or more \code{ind = "all"}
res <- tsicEmp(sample = sample, ind = "all", k = 100, sobol = TRUE, norm = TRUE)
## Select the significative tsic
indices_nonzero <- which(res$tsic %in% boxplot.stats(res$tsic)$out == TRUE)
## Subsets associated with significative tsic reflecting the tail support
as.character(res$subsets[indices_nonzero])
## Pairwise tsic are obtained by
res_pairs <- tsicEmp(sample = sample, ind = 2, k = 100, sobol = TRUE, norm = TRUE)
## and plotted in the tail dependograph
graphsEmp(sample, k = 100)