simu.SEPP | R Documentation |
Simulate a (stable) SEPP model (without change point).
simu.SEPP(intercept, n, A, threshold, vzero = NULL)
intercept |
A |
n |
An |
A |
A |
threshold |
A |
vzero |
A |
A p-by-n matrix.
Daren Wang & Haotian Xu
Wang, Yu, & Willett (2020). Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes. <arXiv:2006.03572>.
p = 10 # dimension n = 50 s = 5 # sparsity factor = 0.12 # large factor gives exact recovery threshold = 4 # thresholding makes the process stable intercept = 1/2 # intercept of the model. Assume to be known as in the existing literature A1 = A2 = A3 = matrix(0, p, p) diag(A1[,-1]) = 1 diag(A1) = 1 diag(A1[-1,]) = -1 A1 = A1*factor A1[(s+1):p, (s+1):p] = 0 diag(A2[,-1]) = 1 diag(A2) = -1 diag(A2[-1,]) = 1 A2 = A2*factor A2[(s+1):p, (s+1):p] = 0 diag(A3[,-1]) = 1 diag(A3) = 1 diag(A3[-1,]) = -1 A3 = A3*factor A3[(s+1):p, (s+1):p] = 0 data1 = simu.SEPP(intercept, n, A1, threshold, vzero = NULL) data2 = simu.SEPP(intercept, n, A2, threshold, vzero = data1[,n]) data3 = simu.SEPP(intercept, n, A3, threshold, vzero = data2[,n]) data = cbind(data1, data2, data3) dim(data)
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