A Novel Hybrid Technique Combining Improved Cepstrum Pre-Whitening and High-Pass Filtering for Effective Bearing Fault Diagnosis Using Vibration Data.

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis.

Exploring the bidirectional causality between green markets and economic policy: evidence from the time-varying Granger test.

The vigorous development of green markets and the effective mitigation of economic policy fluctuations are current hotspots that intrigue our interest in exploring the causal relationships between green market returns and economic policy uncertainty (EPU). Green bonds, corporate environmental responsibility, green technology investment, and the carbon trading market are our research objects to comprehensively understand the interaction among them, from both macro and micro perspectives. Considering the importance of temporal heterogeneity and spillover direction in causation, we employ the time-varying Granger causality method to obtain bidirectional real-time identification.

Analysis of schizophrenia data using a nonlinear threshold index logistic model.

Genetic information, such as single nucleotide polymorphism (SNP) data, has been widely recognized as useful in prediction of disease risk. However, how to model the genetic data that is often categorical in disease class prediction is complex and challenging. In this paper, we propose a novel class of nonlinear threshold index logistic models to deal with the complex, nonlinear effects of categorical/discrete SNP covariates for Schizophrenia class prediction.

Minimum Hellinger distance estimation for finite mixtures of Poisson regression models and its applications.

Minimum Hellinger distance estimation (MHDE) has been shown to discount anomalous data points in a smooth manner with first-order efficiency for a correctly specified model. An estimation approach is proposed for finite mixtures of Poisson regression models based on MHDE. Evidence from Monte Carlo simulations suggests that MHDE is a viable alternative to the maximum likelihood estimator when the mixture components are not well separated or the model parameters are near zero.