depmeasAsympVar: Asymptotic variance of Kendall's tau, Spearman's rho and...

In YafeiXu/CopulaModel: CopulaModel: Dependence Modeling with Copulas

Description

Asymptotic variance of Kendall's tau, Spearman's rho and Blomqvist's beta for a bivariate copula

Usage

ktau.avar(cpar,pcop,pcond12,pcond21,zero=0,tol=1.e-5)
rhoS.avar(cpar,pcop,pcond12,pcond21,nq=25,zero=0,tol=1.e-5)
blomqvist.avar(cpar,pcop)

Arguments

cpar	copula parameter: scalar or vector
pcop	function for bivariate copula cdf
pcond12	function for C_{1|2}(u|v)
pcond21	function for C_{2|1}(v|u)
zero	boundary is [zero,1-zero]^2 for integration, default is zero, but choose something like zero=1.e-6 if there is a boundary problem
tol	desired accuracy for numerical integration, default 0.00001
nq	number of quadrature points per dimension for an inner integral. default is 25

Value

asymptotic variance = avar, so that approximate variance for sample size n is avar/n.

See Also

bivdepmeas

Examples

cpar=2
avarkt=ktau.avar(cpar,pfrk,pcondfrk,pcondfrk)
avarsp=rhoS.avar(cpar,pfrk,pcondfrk,pcondfrk,nq=25)
avarbl=blomqvist.avar(cpar,pfrk)
print(c(cpar,avarkt,avarsp,avarbl))

YafeiXu/CopulaModel documentation built on May 9, 2019, 11:07 p.m.