Huiming Zhu

I. The increasing integration of global financial markets has heightened the complexity of cross-market volatility transmission and the diffusion of systemic risk. Existing research has examined th...

The most difficult aspect in statistical process monitoring is an extensive evaluation of memory‐type charts in zero‐state and steady‐state scenarios. In actual practice, a chart does well at the zero state but poorly at the steady state. Nevertheless, some researchers argued for the superiority of their charts merely based on performance under zero‐state conditions, which may not hold true with respect to long‐term monitoring stability. However, recent literature underlines that a chart can be regarded as superior only if it performs well both in zero‐state and in steady‐state scenarios. The novel Cumulative Sum (CUSUM) chart is accordingly constructed based on variable transformations under an exponential distribution, hence termed . The chart has been developed particularly for maximizing effectiveness in both zero‐state and steady‐state conditions. The Monte Carlo simulations have been carried out to assess the performance and demonstrate the utility of the approach. To thoroughly assess its performance, various traditionally accepted metrics, average run length (ARL), standard deviation of run length (SDRL), median run length (MDRL), extra quadratic loss (EQL), performance comparison index (PCI), and relative ARL (RARL), are applied. The proposed chart is compared with existing alternatives based on the metrics included. The analysis establishes that the is superior to its rivals in terms of shift detection under both zero‐state and steady‐state scenarios. The efficacy of the proposed chart has been illustrated through its application to two real healthcare datasets, which have verified its practicality for process monitoring.

This article introduces a new robust multivariate Hotelling T‐square (TS) control chart that incorporates an L‐Comoments covariance matrix into a multivariate statistical process control (MSPC) charting scheme to enhance its robustness and detection ability. However, among the most popular, conventional Hotelling TS charts are affected by outliers and based on the so‐called classical covariance matrix estimators, which in turn presuppose normality and independence. This sensitivity reduces their usefulness in complicated practical problems where skewness, heavy tails, and outliers are likely to appear. Using L‐Comoments as a basis of the new chart can overcome these limitations since L‐Comoments are not affected by outliers. The performance of the proposed Hotelling TS (HTS) chart is assessed using total and generalized variances. By comparing the effectiveness of the L‐Comoments‐based TS chart using simulated and renewable energy data, the new chart based on the proposed approach outperforms the traditional chart and robust charts based on powerful estimators such as the minimum volume ellipsoid (MVE) and the minimum covariance determinant (MCD). Hence, the new approach enables the new exploration of robust and reliable multivariate quality control analysis for high‐dimension and complex datasets.

This article introduces a novel approach, the triple exponentially weighted moving average (TEWMA) control charting structure, designed to monitor the process coefficient of variation (CV) along three parametric log-normal transformations. The average run length (ARL) and standard deviation of the run length (SDRL) are considered as a performance measures, and both are computed through Monte Carlo simulations. A comparative analysis with an existing control chart enriches the study. For the practical importance of the suggested control charting structure a real-data set from the sanitary sector die-casting process is studied.