Reducing the Bias of Causality Measures

Measures of the direction and strength of the interdependence between two time series are evaluated and modified in order to reduce the bias in the estimation of the measures, so that they give zero values when there is no causal effect. For this, point shuffling is employed as used in the frame of surrogate data. This correction is not specific to a particular measure and it is implemented here on measures based on state space reconstruction and information measures. The performance of the causality measures and their modifications is evaluated on simulated uncoupled and coupled dynamical systems and for different settings of embedding dimension, time series length and noise level. The corrected measures, and particularly the suggested corrected transfer entropy, turn out to stabilize at the zero level in the absence of causal effect and detect correctly the direction of information flow when it is present. The measures are also evaluated on electroencephalograms (EEG) for the detection of the information flow in the brain of an epileptic patient. The performance of the measures on EEG is interpreted, in view of the results from the simulation study.Comment: 30 pages, 12 figures, accepted to Physical Review

Determinants of Central Bank independence: a random forest approach

In this paper we implement an effcient non-parametric statistical method, Random survival forests, for the selection of the determinants of Central Bank Independence (CBI) among a large data base of political and economic variables for OECD countries.This statistical technique enables us to overcome omitted variables and overftting problems. It turns out that the economic variables are major determinants compared to the political ones and linear andnonlinear effects of chosen predictors on CBI are found

Diagnosis of Infection After Splenectomy for Trauma Should be Based on Lack of Platelets Rather Than White Blood Cell Count

Background: There is a lack of evidence-based criteria to assist the diagnosis of infection following trauma splenectomy (TS). However, the literature suggests that white blood cell count (WBC) is associated with infection in patients who undergo TS. We sought to find whether there exist key differences in laboratory and clinical parameters that can assist the diagnosis of infection after TS. Methods: We evaluated all consecutive trauma patients who had undergone TS at a Level 1 trauma center from 2005 to 2011 for the development of infection. To do this, we compared the values of demographic, laboratory, and clinical variables of infected and non-infected patients on odd post-operative days (POD) in the period from 1–15 days after TS. Results: Of 127 patients who underwent TS, 25 died within 48 h after the procedure and were excluded from our analysis, leaving, 102 patients for investigation. In the 41 (40%) patients who developed an infection, the mean day for the first infectious episode was POD 7 (range, POD 4–14). The three most common infections were pneumonia (51%), urinary tract infection (24%), and bacteremia (20%). An evaluation of laboratory and clinical parameters showed no differences in the WBC of the patients who did and did not develop infections at any time in the 15 d after TS. However, the platelet count was statistically significantly higher in non-infected patients on POD 3–9 and on POD 13, and maximal body temperature was statistically significantly higher in the infected group of patients during the first week after TS. Differences in laboratory and clinical values of the infected and non-infected patients were greatest on POD 5. Conclusions: Patients who undergo TS have high rates of infectious complications. The WBC is not a reliable predictor of infection in these patients in the 2 wks following TS. However, patients who do not develop infection after TS have statistically significantly higher absolute platelet counts and rates of change in their daily platelet counts than those who develop infection

Diagnosis of Infection After Splenectomy for Trauma Should be Based on Lack of Platelets Rather Than White Blood Cell Count

Background: There is a lack of evidence-based criteria to assist the diagnosis of infection following trauma splenectomy (TS). However, the literature suggests that white blood cell count (WBC) is associated with infection in patients who undergo TS. We sought to find whether there exist key differences in laboratory and clinical parameters that can assist the diagnosis of infection after TS. Methods: We evaluated all consecutive trauma patients who had undergone TS at a Level 1 trauma center from 2005 to 2011 for the development of infection. To do this, we compared the values of demographic, laboratory, and clinical variables of infected and non-infected patients on odd post-operative days (POD) in the period from 1–15 days after TS. Results: Of 127 patients who underwent TS, 25 died within 48 h after the procedure and were excluded from our analysis, leaving, 102 patients for investigation. In the 41 (40%) patients who developed an infection, the mean day for the first infectious episode was POD 7 (range, POD 4–14). The three most common infections were pneumonia (51%), urinary tract infection (24%), and bacteremia (20%). An evaluation of laboratory and clinical parameters showed no differences in the WBC of the patients who did and did not develop infections at any time in the 15 d after TS. However, the platelet count was statistically significantly higher in non-infected patients on POD 3–9 and on POD 13, and maximal body temperature was statistically significantly higher in the infected group of patients during the first week after TS. Differences in laboratory and clinical values of the infected and non-infected patients were greatest on POD 5. Conclusions: Patients who undergo TS have high rates of infectious complications. The WBC is not a reliable predictor of infection in these patients in the 2 wks following TS. However, patients who do not develop infection after TS have statistically significantly higher absolute platelet counts and rates of change in their daily platelet counts than those who develop infection

Mutual information rate and bounds for it

The amount of information exchanged per unit of time between two nodes in a dynamical network or between two data sets is a powerful concept for analysing complex systems. This quantity, known as the mutual information rate (MIR), is calculated from the mutual information, which is rigorously defined only for random systems. Moreover, the definition of mutual information is based on probabilities of significant events. This work offers a simple alternative way to calculate the MIR in dynamical (deterministic) networks or between two data sets (not fully deterministic), and to calculate its upper and lower bounds without having to calculate probabilities, but rather in terms of well known and well defined quantities in dynamical systems. As possible applications of our bounds, we study the relationship between synchronisation and the exchange of information in a system of two coupled maps and in experimental networks of coupled oscillators

Assessment of resampling methods for causality testing

Different resampling methods for the null hypothesis of non-causality are assessed. As test statistic the partial transfer entropy (PTE), an information and model-free measure, is used. Two resampling techniques,1) the time shifted surrogates and 2) the stationary bootstrap, are combined with the following three independence settings (giving in total six resampling schemes), all consistent to the null hypothesis of non-causality: A) only the driving variable is resampled, B) both the driving and response variable are independently resampled, and C) both the driving and response variable are resampled while also the dependence of the future of the response variable and the vector of its past values is destroyed. The empirical null distribution of the PTE as the surrogate and bootstrapped time series become more independent is examined along with the size and power of the respective tests. Further, we consider the resampling method of contemporaneously resampling the driving and the response time series using the stationary bootstrap. Although this resampling method does not comply with the non-causality hypothesis, one can obtain an accurate sampling distribution of the mean of the test statistic since the mean value of the test statistic is zero under H0. This resampling scheme performs well in terms of size and power, provided that the null distribution of the bootstrap values of the test statistic is shifted to have mean zero