MDPD: The Minimum Distance Power Divergence statistics

In RTDE: Robust Tail Dependence Estimation

The Minimum Distance Power Divergence statistics

Description

Computes the power divergence statistics then used a minimization problem.

Usage

MDPD(theta, densfun, obs, alpha, ..., control=list())

Arguments

theta	the parameter of the distribution given as a vector.
densfun	a function computing the theoretical density function.
obs	a numeric vector of observations
alpha	a numeric for the power divergence parameter.
...	further arguments to be passed to the density function.
control	A list of control paremeters. See section Details.

Details

The Power Divergence for a density function f and observations X_1,...,X_n is defined as

\Delta(f,\alpha) = \int_{R} f^{1+\alpha}(x)dx-\left ( 1+\frac{1}{\alpha} \right ) \frac{1}{n} \sum_{i=1}^n f^\alpha(X_i)

for \alpha> 0

\Delta(f,0) = -\frac{1}{n}\sum_{i=1}^n \log f(X_i)

for \alpha = 0.

The control argument is a list that can supply any of the following components:

eps

a small positive floating-point number used when integrate stalled, default to 1e-3.

tol

the desired accuracy used in the integrate function when computing the power divergence, default to 1e-3.

lower

the lower bound of the domain of the density function, default to 1.

upper

the lower bound of the domain of the density function, default to infinity.

Value

MDPD returns the power divergence against the density function densfun as a numeric.

Author(s)

Christophe Dutang

References

Basu, A., Harris, I.R., Hjort, N.L., Jones, M.C., (1998). Robust and efficient estimation by minimizing a density power divergence, Biometrika, 85, 549-559.

C. Dutang, Y. Goegebeur, A. Guillou (2014), Robust and bias-corrected estimation of the coefficient of tail dependence, Insurance: Mathematics and Economics

This work was supported by a research grant (VKR023480) from VILLUM FONDEN and an international project for scientific cooperation (PICS-6416).

Examples

#####
# (1) small example

omega <- 1/2
m <- 10
n <- 100
obs <- cbind(rupareto(n), rupareto(n)) + rupareto(n)

#unit Pareto transform
z <- zvalueRTDE(obs, omega, nbpoint=m, output="relexcess")

MDPD(c(1/2, 1/4), dEPD, z$Z, alpha=0, rho=-1)




		

RTDE documentation built on Oct. 16, 2024, 5:08 p.m.