estMultiExpectiles {ExtremeRisks} | R Documentation |
Computes point estimates and (1-α)100\% confidence regions for d
-dimensional expectiles at the intermediate level.
estMultiExpectiles(data, tau, method="LAWS", tailest="Hill", var=FALSE, varType="asym-Ind-Adj", k=NULL, alpha=0.05, plot=FALSE)
data |
A matrix of (n x d) observations. |
tau |
A real in (0,1) specifying the intermediate level τ_n. See Details. |
method |
A string specifying the method used to estimate the expecile. By default |
tailest |
A string specifying the type of tail index estimator. By default |
var |
If |
varType |
A string specifying the asymptotic variance-covariance matrix to compute. By default |
k |
An integer specifying the value of the intermediate sequence k_n. See Details. |
alpha |
A real in (0,1) specifying the confidence level (1-α)100\% of the approximate confidence region for the |
plot |
A logical value. By default |
For a dataset data
of d
-dimensional observations and sample size n, an estimate of the τ_n-th d
-dimensional is computed. Two estimators are available: the so-called direct Least Asymmetrically Weighted Squares (LAWS) and indirect Quantile-Based (QB). The QB estimator depends on the estimation of the d
-dimensional tail index γ. Here, γ is estimated using the Hill estimator (see MultiHTailIndex). The data are regarded as d
-dimensional temporal independent observations coming from dependent variables. See Padoan and Stupfler (2020) for details.
The so-called intermediate level tau
or τ_n is a sequence of positive reals such that τ_n -> 1 as n -> ∞. Practically, for each individual marginal distribution τ_n in (0,1) is the ratio between N (Numerator) and D (Denominator). Where N is the empirical mean distance of the τ_n-th expectile from the observations smaller than it, and D is the empirical mean distance of τ_n-th expectile from all the observations.
If method='LAWS'
, then the expectile at the intermediate level τ_n is estimated applying the direct LAWS estimator. Instead, If method='QB'
the indirect QB esimtator is used to estimate the expectile. See Section 2.1 in Padoan and Stupfler (2020) for details.
When the expectile is estimated by the indirect QB esimtator (method='QB'
), an estimate of the d
-dimensional tail index γ is needed. Here the d
-dimensional tail index γ is estimated using the d
-dimensional Hill estimator (tailest='Hill'
, see MultiHTailIndex). This is the only available option so far (soon more results will be available).
k
or k_n is the value of the so-called intermediate sequence k_n, n=1,2,.... Its represents a sequence of positive integers such that k_n -> ∞ and k_n/n -> 0 as n -> ∞. Practically, for each marginal distribution, when method='LAWS'
and tau=NULL
, k_n specifies by tau_n = 1 - k_n / n the intermediate level of the expectile. Instead, for each marginal distribution, when method='QB'
, then the value k_n specifies the number of k
+1 larger order statistics to be used to estimate γ by the Hill estimator and if tau=NULL
then it also specifies by τ_n=1-k_n/n the confidence level τ_n of the quantile to estimate.
If var=TRUE
then an estimate of the asymptotic variance-covariance matrix of the d
-dimensional expecile estimator is computed. If the data are regarded as d
-dimensional temporal independent observations coming from dependent variables. Then, the asymptotic variance-covariance matrix is estimated by the formulas in section 3.1 of Padoan and Stupfler (2020). In particular, the variance-covariance matrix is computed exploiting the asymptotic behaviour of the relative explectile estimator appropriately normalized and using a suitable adjustment. This is achieved through varType="asym-Ind-Adj"
. The data can also be regarded as coded-dimensional temporal independent observations coming from independent variables. In this case the asymptotic variance-covariance matrix is diagonal and is also computed exploiting the formulas in section 3.1 of Padoan and Stupfler (2020). This is achieved through varType="asym-Ind"
.
Given a small value α\in (0,1) then an asymptotic confidence region for the τ_n-th expectile, with approximate nominal confidence level (1-α)100\% is computed. In particular, a "symmetric" confidence regions is computed exploiting the asymptotic behaviour of the relative explectile estimator appropriately normalized. See Sections 3.1 of Padoan and Stupfler (2020) for detailed.
If plot=TRUE
then a graphical representation of the estimates is not provided.
A list with elements:
ExpctHat
: an point estimate of the τ_n-th d
-dimensional expecile;
biasTerm
: an point estimate of the bias term of the estimated expecile;
VarCovEHat
: an estimate of the asymptotic variance of the expectile estimator;
EstConReg
: an estimate of the approximate (1-α)100\% confidence region for τ_n-th d
-dimensional expecile.
Simone Padoan, simone.padoan@unibocconi.it, http://mypage.unibocconi.it/simonepadoan/; Gilles Stupfler, gilles.stupfler@ensai.fr, http://ensai.fr/en/equipe/stupfler-gilles/
Padoan A.S. and Stupfler, G. (2020). Joint inference on extreme expectiles for multivariate heavy-tailed distributions. arXiv e-prints arXiv:2007.08944, https://arxiv.org/abs/2007.08944
MultiHTailIndex, predMultiExpectiles, extMultiQuantile
# Extreme expectile estimation at the intermediate level tau obtained with # d-dimensional observations simulated from a joint distribution with # a Gumbel copula and equal Frechet marginal distributions. library(plot3D) library(copula) library(evd) # distributional setting copula <- "Gumbel" dist <- "Frechet" # parameter setting dep <- 3 dim <- 3 scale <- rep(1, dim) shape <- rep(3, dim) par <- list(dep=dep, scale=scale, shape=shape, dim=dim) # Intermediate level (or sample tail probability 1-tau) tau <- .95 # sample size ndata <- 1000 # Simulates a sample from a multivariate distribution with equal Frechet # marginals distributions and a Gumbel copula data <- rmdata(ndata, dist, copula, par) scatter3D(data[,1], data[,2], data[,3]) # High d-dimensional expectile (intermediate level) estimation expectHat <- estMultiExpectiles(data, tau, var=TRUE) expectHat$ExpctHat expectHat$VarCovEHat # run the following command to see the graphical representation expectHat <- estMultiExpectiles(data, tau, var=TRUE, plot=TRUE)