kn {npcopTest} | R Documentation |
Give an estimation of the abrupt change point in the copula when changes known occurs in the m.c.d.f.
kn(X,b)
X |
a (non-empty) numeric matrix of d-dimensional data values, d≥q 2. Each row of the matrix contains one mutlivariate data. |
b |
a single value or a vector of real values on (0,1] indicating the location(s) of the potential break time(s) in marginal cumulative distribution functions. You can specify b=1 (default) for any break time. |
Estimation of the location of the abrupt change point in copula
estimation of the location of the change point in the copula
Rohmer Tom
Tom Rohmer, Some results on change-point detection in cross-sectional dependence of multivariate data with changes in marginal distributions, Statistics & Probability Letters, Volume 119, December 2016, Pages 45-54, ISSN 0167-7152
#Example 1: Abrupt change in the m.c.d.f at time (known) m=50 # and in the copula at time k=50 (to be estimated) n=100 m=50 mean1 = rep(0,2) mean2 = rep(4,2) sigma1 = matrix(c(1,0.2,0.2,1),2,2) sigma2 = matrix(c(1,0.6,0.6,1),2,2) X=matrix(rep(0,n*2),n,2) for(j in 1:m) X[j,]=t(chol(sigma1))%*%rnorm(2) + mean1 for(j in (m+1):n) X[j,]=t(chol(sigma2))%*%rnorm(2) + mean2 kn(X,b=0.5) #Example 2: Abrupt changes in the m.c.d.f at times (known) m=100 and 150 # and in the copula at time k=50 (to be estimated) n=200 m1 = 100 m2 = 150 k = 50 sigma1 = matrix(c(1,0.2,0.2,1),2,2) sigma2 = matrix(c(1,0.6,0.6,1),2,2) mean1 = rep(0,2) mean2 = rep(2,2) mean3 = rep(4,2) X=matrix(rep(0,n*2),n,2) for(j in 1:k) X[j,]=t(chol(sigma1))%*%rnorm(2) for(j in (k+1):n) X[j,]=t(chol(sigma2))%*%rnorm(2) X[1:m1,]=X[1:m1,]+mean1 X[(m1+1):m2,]=X[(m1+1):m2,]+mean2 X[(m2+1):n,]=X[(m2+1):n,]+mean3 kn(X,b=c(0.5,0.75))