Selected article for: "critical transition and Kendall tau score"

Author: Emma Southall; Michael J. Tildesley; Louise Dyson
Title: Prospects for detecting early warning signals in discrete event sequence data: application to epidemiological incidence data
  • Document date: 2020_4_2
  • ID: dp4qv77q_58
    Snippet: Here, we quantify these time series trends for each statistical indicator using the 431 Kendall-Tau score as a measure of an overall increasing or decreasing trend. We present 432 in Fig. 3 the predictive power of each statistical indicator using its time-changing trend 433 to classify simulations as either extinct (Ext simulations), emerging (Emg simulations) 434 or null simulations (Fix simulations). We calculate the area under the ROC curve 43.....
    Document: Here, we quantify these time series trends for each statistical indicator using the 431 Kendall-Tau score as a measure of an overall increasing or decreasing trend. We present 432 in Fig. 3 the predictive power of each statistical indicator using its time-changing trend 433 to classify simulations as either extinct (Ext simulations), emerging (Emg simulations) 434 or null simulations (Fix simulations). We calculate the area under the ROC curve 435 (AUC) by comparing the Kendall-tau score calculated over each time series up to two 436 end points: before the critical transition (t 1 ) and after the critical transition (t 2 ) which 437 gives an overall score of the true/false positive rate.

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