Advances in Data Science and Adaptive Analysis

We propose a new class of survival models for time-to-event data with a cure fraction. This new model is an extension of the promotion time cure rate model. Furthermore, we extend the model to the regression model to evaluate the effect of covariates in the cure fraction. An expectation-maximization algorithm is adopted for estimating the model parameters. A simulation study is conducted in order to assess the proposed model and the computation algorithm. The methodology is illustrated using a real Brazilian bank personal loan portfolio data set.

The cardiovascular diseases are the major cause of death globally. To diagnose heart disease, automatic recognition of ECG’s T-wave is necessary. Empirical mode decomposition (EMD) can be used to decompose nonlinear and nonstationary signals. However, using EMD to decompose ECG potentially leads to a mode mixing problem. This study proposes modulated EEMD (mEEMD) as a solution, which can solve mode mixing problems with almost no influence from noise. Furthermore, the mEEMD has a less problematic boundary side effect and does not cause any phase shift. The sensitivity of T-wave onset and offset recognition is $0.73\pm 0.29$ and $0.78\pm 0.25$.

In a previous study, we solved the two-fold dynamic ambulance allocation problem, including forecasting the distribution of Emergency Medical Service (EMS) requesters and dynamically allocating ambulances according to the predicted distribution of requesters. In the definition of the coverage region, the Euclidean distance was used, which is not suitable for measuring the length of a route between two places. This study improved on the previous one by redefining the coverage region for practical application and providing a simulation model to verify the effectiveness of the proposed ambulance allocation method. The simulation results show the proposed allocation method providing higher demand coverage rates and shorter response distances than the official allocation.

Dempster’s combination rule may produce some unreasonable results when dealing with a combination of the conflicting evidence in evidence theory of Dempster–Shafer. Therefore, analyzing the degree of conflict between the bodies of evidence is essential to evaluate the applicability of Dempster’s rule. A new probability function, which is called a supporting probability function, is proposed to describe the correlation between evidences, and its distance is proposed to measure the distance between bodies of evidence. Combining this distance with classical conflict coefficient, a new method of evaluating the applicability of classical Dempster’s combination rule is presented. A weighted average approach to combine the conflicting evidences based on a supporting probability distance between the bodies of evidence is proposed. Numerical examples are given to illustrate the interest of the proposed approach.