DP.SEPP | R Documentation |
Perform dynamic programming for SEPP change points detection.
DP.SEPP(DATA, gamma, lambda, delta, delta2, intercept, threshold)
DATA |
A |
gamma |
A |
lambda |
A |
delta |
An |
delta2 |
An |
intercept |
A |
threshold |
A |
An object of class
"DP", which is a list
with the following structure:
partition |
A vector of the best partition. |
cpt |
A vector of change points estimation. |
Daren Wang & Haotian Xu
Wang, D., Yu, Y., & Willett, R. (2020). Detecting Abrupt Changes in High-Dimensional Self-Exciting Poisson Processes. arXiv preprint arXiv:2006.03572.
p = 8 # dimension n = 15 s = 3 # s is sparsity factor = 0.2 # 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 data1 = simu.SEPP(intercept, n, A1, threshold, vzero = NULL) data2 = simu.SEPP(intercept, n, A2, threshold, vzero = data1[,n]) data = cbind(data1, data2) gamma = 0.1 delta = 0.5*n delta2 = 1.5*n intercept = 1/2 threshold = 6 DP_result = DP.SEPP(data, gamma = gamma, lambda = 0.03, delta, delta2, intercept, threshold) cpt_hat = DP_result$cpt
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