Search

Purpose: This study aimed to assess the learning curve of robot-assisted intersphincteric resection for low rectal cancer. Methods: We retrospectively analyzed the clinical data of 89 patients who underwent robot-assisted intersphincteric resection. All surgeries were performed by the same group of surgeons at our institution between June 2016 and April 2021. The learning curve was evaluated using a cumulative sum analysis and the best-fit curve. The different stages of the learning curve were compared based on patient characteristics and short-term clinical outcomes to evaluate their impact on clinical efficacy. Results: The minimum number of cases required to overcome the learning curve was 47. The learning curve was divided into the learning improvement and proficiency stages. Significant differences were observed in the operation time and the number of lymph nodes between the two stages (P < 0.05), whereas no significant differences were found in intraoperative blood loss, first postoperative exhaust time, postoperative complications, 3-year progression-free survival, overall survival, and local recurrence-free survival (P > 0.05). Conclusion: Robotic-assisted intersphincteric resection for low rectal cancer exhibits a learning curve that can be divided into two stages: namely, learning improvement and proficiency. Achieving proficiency requires a minimum of 47 surgical cases. [ABSTRACT FROM AUTHOR]

Outcome monitoring can identify fluctuations in healthcare. Cumulative sum (CUSUM) analysis can detect when mortality deviates from expected, allowing early intervention through targeted audit. We present a 10-year experience of the prospective use of CUSUM methodology in a regional burn centre. Prospective outcome monitoring was conducted for all admissions to the intensive care unit between 2012 and 2022. The revised Baux score was used for mortality risk prediction. Risk-adjusted CUSUM charts tracked mortality against that predicted by the revised Baux score. Deviations from expected outcomes triggered detailed structured analysis. Learning outcomes were identified from internal and external governance groups. CUSUM analysis was triggered on eight occasions: one paediatric (excess deaths), six adult (four excess survivors, two excess deaths) and one elderly (excess survivors). Detailed analysis identified areas for continuous improvement and positive themes from excess survivors. The use of CUSUM as an early warning trigger stimulates assessment of practice and critical appraisal of factors contributing to unexpected mortality or survival. The revised Baux score at its foundation needs to be carefully considered but remains a valid model. One benefit is positive reinforcement of team cohesion and morale during periods of care excellence leading to excess survivors. • CUSUM analysis is a very effective tool for highlighting emerging changes to expected outcome on a monthly basis. • CUSUM analysis stimulates early review andcritical appraisal of factors contributing to deviations from expected outcomes. • CUSUM methodology can identify periods of excellence and enable the underlying principles and practices to be reinforced. [ABSTRACT FROM AUTHOR]

In the field of quality control, statistical process control (SPC) has its significance. The control charts are important tools of the SPC to observe the outputs of the production process. The cumulative sum (CUSUM) charting structure is one such technique that is designed to identify the medium and slight changes in the process parameters. In the literature, the assumption of normality is considered ideal for the control chart but in practice, many quality characteristics do not follow the assumption of normality. The current study proposes a class of CUSUM control charts for the location parameter of the process and investigates the performance of said location charts based on ranked set sampling (RSS) for quality characteristics having different environments. Different point estimators of location are considered in this study to develop the location control charts under normal, and a variety of non‐normal environments. The numerical results show that the newly designed schemes perform uniformly well than their competitors. A real‐life example linked with the manufacturing process is also provided for the practical implementation of the proposed scheme. [ABSTRACT FROM AUTHOR]

To improve the detecting abilities of upward and downward parameter changes in high‐quality process, two combined schemes by integrating memory‐type, that is, exponentially weighted moving average (EWMA) or cumulative sum (CUSUM), and memoryless, that is, Shewhart, are proposed for monitoring the time between events (TBE), which is modeled as an exponential distributed variable. A Monte Carlo simulation method is employed to obtain the Run Length (RL$RL$) properties, that is average run length (ARL$ARL$), of the proposed schemes for different parameter settings. The nearly optimal monitoring parameter combinations under different change size are obtained by minimizing the out‐of‐control ARL$ARL$ with the satisfied in‐control ARL$ARL$. Using the designed parameters, the ARL$ARL$ performance of the proposed monitoring schemes is compared with the existing EWMA and CUSUM TBE. The results show that the proposed combined Shewhart–EWMA or Shewhart–CUSUM TBE generally perform better than the corresponding EWMA or CUSUM TBE for large changes and they also show better performance than the Shewhart TBE for small changes. Finally, a real dataset of organic light‐emitting diode (OLED) failure time from Sumsung company is employed to indicate the usage and implementation of combined TBE schemes. [ABSTRACT FROM AUTHOR]

Various statistical approaches exist to delineate learning curves in spine surgery. Techniques range from dividing cases into intervals for metric comparison, to employing regression and cumulative summation (CUSUM) analyses. However, their inherent inconsistencies and methodological flaws limit their comparability and reliability. To critically evaluate the methodologies used in existing literature for studying learning curves in spine surgery and to provide recommendations for future research. Systematic literature review. A comprehensive literature search was conducted using PubMed, Embase, and Scopus databases, covering articles from January 2010 to September 2023. For inclusion, articles had to evaluate the change in a metric of performance during human spine surgery across time/a case series. Results had to be reported in sufficient detail to allow for evaluation of individual performance rather than group/institutional performance. Articles were excluded if they included cadaveric/nonhuman subjects, aggregated performance data or no way to infer change across a number of cases. Risk of bias was assessed using the Risk of Bias in Nonrandomized Studies of Interventions (ROBINS-I) tool. Surgical data were simulated using Python 3 and then examined via multiple commonly used analytic approaches including division into consecutive intervals, regression and CUSUM techniques. Results were qualitatively assessed to determine the effectiveness and limitations of each approach in depicting a learning curve. About 113 studies met inclusion criteria. The majority of the studies were retrospective and evaluated a single-surgeon's experience. Methods varied considerably, with 66 studies using a single proficiency metric and 47 using more than 1. Operating time was the most commonly used metric. Interval division was the simplest and most commonly used method yet inherent limitations prevent collective synthesis. Regression may accurately describe the learning curve but in practice is hampered by sample size and model choice. CUSUM analyses are of widely varying quality with some being fundamentally flawed and widely misinterpreted however, others provide a reliable view of the learning process. There is considerable variation in the quality of existing studies on learning curves in spine surgery. CUSUM analyses, when correctly applied, offer the most reliable estimates. To improve the validity and comparability of future studies, adherence to methodological guidelines is crucial. Multiple or composite performance metrics are necessary for a holistic understanding of the learning process. [ABSTRACT FROM AUTHOR]

Current monitoring technologies emphasize and address the issue of monitoring high-volume production processes. The high flexibility and diversity of current industrial production processes make monitoring technology for small batch processes even more important. In multivariate process monitoring, a broader applicability exists in multivariate coefficients of variation (MCV) based monitoring schemes due to the lower restriction of the process. In view of the effectiveness of MCV monitoring and with the aim to achieve further performance improvement of current MCV monitoring schemes in a finite horizon production, we additionally introduce two one-sided cumulative sum (CUSUM) MCV schemes. In the case of deterministic and random shifts, the design parameters of the proposed schemes are obtained via an optimization procedure designed by the Markov chain method and the corresponding performance is analysed based on different run length (RL) characteristics, including the mean and the standard deviation. Simulation comparisons with existing exponentially weighted moving average (EWMA) MCV schemes show that the proposed CUSUM MCV schemes are more efficient in monitoring most of the shifts, including the deterministic and random shifts. Finally, to demonstrate the benefits of the new monitoring schemes, a comprehensive case study on monitoring a steel sleeve manufacturing process is conducted. [ABSTRACT FROM AUTHOR]

An unsupervised change point detection (CPD) framework assisted by a predictive machine learning model called "Predict and Compare" is introduced which is able to detect change points online under the presence of non-trivial trend patterns which must be prevented from triggering false positives. Different predictive models for the required time series forecasting (Predict) step together with different statistical tests for deciding about the proximity of predicted and actual data (Compare step) are allowed. Its performance is shown for the Predict step being carried out by either an LSTM recursive neural network or an ARIMA linear time series model together with the CUSUM rule as Compare step method. It shows to perform best in comparison to several other online CPD methods for detect times in the regime of low numbers of false positive detections. The method's good performance is based on its ability to detect structural changes in the presence complex underlying trend patterns. The use case concerns tribological wear for which change points separating the run-in, steady-state, and divergent wear phases are detected. [ABSTRACT FROM AUTHOR]

AbstractThe traditional time-between-events (TBE) control charts are developed in non-adaptive fashion assuming the Poisson process, where the TBE follows the exponential distribution. However, in many situations we need an adaptive strategy, especially for healthcare or system monitoring. The focus of this study is to introduce new exponentially weighted moving average and cumulative sum adaptive TBE control charts for high-quality processes based on nonhomogeneous Poisson process by assuming the power law intensity. Charts assuming the nonhomogeneous Poisson process are dynamic and can be used where the underlying failure rate may be changing over time. The performance of the proposed control charts is evaluated using the average run length and coefficient of variation of the run length distribution. Three real data examples which correspond to different shape parameters of the power law process are also discussed in the article. [ABSTRACT FROM AUTHOR]

Prior work has demonstrated utility in using operative time to measure surgeon learning for surgical stabilization of rib fractures (SSRF); however, no studies have used operative time to evaluate the benefit of proctoring in subsequent generations of surgeons. We sought to evaluate whether there is a difference in learning between an original series (TOS) of self-taught surgeons versus the next generation (TNG) of proctored surgeons using cumulative summation (CUSUM) analysis. We hypothesized that TNG would have a comparatively accelerated learning curve. A single-center retrospective review of all SSRF at a level 1 trauma center was performed. Data were collected from the beginning of an operative chest injury program to include at least 2 y of TNG experience. Operative time was used to determine success and misstep based on prior methods. Learning curves using CUSUM analysis were calculated based on an anticipated success rate of 90% and compared between TOS and TNG groups. Over 7 y, 163 patients with a median Injury Severity Score of 24 underwent SSRF. Median operative time was 165 min with a 0.5 plate-to-fracture ratio. All three TOS surgeons experienced a positive slope indicative of early missteps for their first 15-20 cases. By contrast, all three TNG surgeons demonstrated a series of early successes resulting in negative CUSUM slopes which coincided with a period of proctoring. By the end of TNG series, the composite cumulative score was less than half of the TOS surgeon' scores. Operative time continues to be a useful surrogate for observing SSRF learning curves. In a mature institutional program, proctored novice surgeons appear to have an accelerated learning curve compared to novice surgeons developing a new operative rib program. [ABSTRACT FROM AUTHOR]

The quality assurance of the earth-atmosphere system, which runs primarily to temperature variations and other disturbances of the earth's climate, compels the assessment of greenhouse gases (GHG) emissions due to various Organizations for Economic Co-operation and Development (OECD) countries' human activities that disturb the radioactive energy balance. The purpose of this paper is to thus determine the usefulness and acceptability of statistical process control for assessing the level of GHG emissions from OECD countries. We apply the exponentially weighted moving average (EWMA) control charts with one-time, two-time, and three-time smoothing processes for monitoring the process variance under a repetitive sampling scheme, named S 2 -EWMA RS , S 2 -DEWMA RS and S 2 -TEWMA RS charts, and also utilize a cumulative sum (CUSUM) control chart under a repetitive sampling scheme, named as S2-CUSUMRS chart, to assess these countries' emission levels of GHG. The control charts are a graphical representation of the trend product of sequential procedures. All the OECD countries can thus independently measure their emission level of GHG as a self-assessment tool. Consequently, we recommend use of the advanced control charts as a tool for OECD assessment at an individual country level. [ABSTRACT FROM AUTHOR]

In this paper, our primary attention was centered on the issue of detecting the variance change point for strong-mixing samples. We delved into the cumulative sum (CUSUM) estimator of variance change model and established the strong convergence rate of the variance change point estimation. Furthermore, to corroborate the effectiveness of the CUSUM based methodology, we have conducted a series of simulations, the outcomes of which underscored its validity.

Abstract Background Gasless transaxillary endoscopic thyroidectomy (GTET) and endoscopic thyroidectomy via the areola approach (ETA) have emerged as minimally invasive surgical techniques for managing papillary thyroid carcinoma (PTC). This study aimed to assess the surgical efficacy of endoscopic thyroidectomy (ET) as compared to conventional open thyroidectomy (COT) in PTC patients. Methods Between 2020 and 2022, 571 PTC patients underwent unilateral thyroidectomy accompanied by ipsilateral central lymph node dissection. This cohort comprised 72 patients who underwent GTET, 105 ETA, and 394 COT. The analysis encompassed a comprehensive examination of patient clinicopathologic characteristics and postoperative complaints. Furthermore, the learning curve of GTET was evaluated using the cumulative summation (CUSUM) method. Results Patients in the ET group exhibited a lower mean age and a higher proportion of female individuals. Operation time in the ET group was significantly longer. No significant differences were observed in the incidence of postoperative complications among the three groups. With regard to postoperative complaints reported three months after surgery, GTET demonstrated superior alleviation of anterior chest discomfort and swallowing difficulties. Patients who underwent ET reported significantly higher cosmetic satisfaction levels. Additionally, the learning curve of GTET was 27 cases, and the operation time during the mature phase of the learning curve exhibited a significant reduction when compared to ETA. Conclusions The findings of this study affirm the safety and feasibility of employing GTET and ETA for the surgical management of PTC. GTET presents an attractive surgical option, particularly for patients with unilateral PTC who place a premium on cosmetic outcomes.

Gary Lorden provided several fundamental and novel insights into sequential hypothesis testing and change point detection. In this article, we provide an overview of Lorden's contributions in the context of existing results in those areas and some extensions made possible by Lorden's work. We also mention some of Lorden's significant consulting work, including as an expert witness and for NASA, the entertainment industry, and Major League Baseball. [ABSTRACT FROM AUTHOR]

Background: Gasless transaxillary endoscopic thyroidectomy (GTET) and endoscopic thyroidectomy via the areola approach (ETA) have emerged as minimally invasive surgical techniques for managing papillary thyroid carcinoma (PTC). This study aimed to assess the surgical efficacy of endoscopic thyroidectomy (ET) as compared to conventional open thyroidectomy (COT) in PTC patients. Methods: Between 2020 and 2022, 571 PTC patients underwent unilateral thyroidectomy accompanied by ipsilateral central lymph node dissection. This cohort comprised 72 patients who underwent GTET, 105 ETA, and 394 COT. The analysis encompassed a comprehensive examination of patient clinicopathologic characteristics and postoperative complaints. Furthermore, the learning curve of GTET was evaluated using the cumulative summation (CUSUM) method. Results: Patients in the ET group exhibited a lower mean age and a higher proportion of female individuals. Operation time in the ET group was significantly longer. No significant differences were observed in the incidence of postoperative complications among the three groups. With regard to postoperative complaints reported three months after surgery, GTET demonstrated superior alleviation of anterior chest discomfort and swallowing difficulties. Patients who underwent ET reported significantly higher cosmetic satisfaction levels. Additionally, the learning curve of GTET was 27 cases, and the operation time during the mature phase of the learning curve exhibited a significant reduction when compared to ETA. Conclusions: The findings of this study affirm the safety and feasibility of employing GTET and ETA for the surgical management of PTC. GTET presents an attractive surgical option, particularly for patients with unilateral PTC who place a premium on cosmetic outcomes. [ABSTRACT FROM AUTHOR]

We consider the detection of multiple change-points in a high-dimensional time series exhibiting both cross-sectional and temporal dependence. Several test statistics based on the celebrated CUSUM statistic are used and discussed. In particular, we propose a novel block wild bootstrap method to address the presence of cross-sectional and temporal dependence. Furthermore, binary segmentation and the moving sum algorithm are considered to detect and locate multiple change-points. We also give some theoretical justifications for the moving sum method. An extensive numerical study provides insights on the performance of the proposed methods. Finally, our proposed procedures are used to analyze financial stock data in Korea. [ABSTRACT FROM AUTHOR]

AbstractThis article studies the CUSUM estimators of the mean change-point based on the association errors and obtains some important consistency results. For example, the weak and strong convergence rates as well as the limit distribution are obtained for the estimators of the mean change-point and mean parameter. We extend the existing results of mean change points, and give some simulations with Normality and multivariate t distribution errors. As an application, two real data examples are provided to illustrate the analysis of the mean change-point. [ABSTRACT FROM AUTHOR]

In the recent years, extreme temperature i.e. cold in winter and warm in summer has been observed in the coastal Odisha state in the east part of India. Hydro-meteorological disasters such as floods, droughts, heat waves and cold spells being experienced nowadays are unprecedented in living memory. In this work the climate variability over the coastal Odisha region is quantified considering the temperature parameter. A climate departure index (CDI) of three versions (simple, absolute and least-square) are calculated using monthly, annually and seasonally maximum, minimum as well as mean temperature form seven stations in the costal Odisha for the period 1969 to 2018 for assessing the degree of normal/unusual/extreme of a particular year in compared to the long-term average for the specific coast under consideration. Temperature time series data for the period 1969 to 2018 are homogenized by using the relative comparison between station observations and composite reference series by calculating CDI. Results indicates that CDI values of least-square on all cases are comparatively greater than simple and absolute CDIs. Least-square method is the appropriate method for determining the category of a year or a month or a season in terms of the temperature variation. Analysis based on mean temperature infers that only twenty extreme seasons are observed in monsoon season while three normal are observed during the month of October i.e., in post-monsoon season over the coastal Odisha. The relationship of climatic event including El Nino or La Nina feature of a particular year with respect to CDI is also investigated. The climatic variability is very high on some of the years based on analysis of index values of annual average of the temperatures (maximum, minimum and mean) over a region. [ABSTRACT FROM AUTHOR]

HUMS (Health and Usage Monitoring Systems) provide enhanced safety and availability by alerting maintenance personnel when components (shaft/gears/bearings) have degraded or become damaged. Proactive maintenance ensures that a degraded component will not fail in flight. Often, the damage is from high cycle fatigue or other relatively slow degradation processes. However, some events, such as maintenance, result in a step-change in the component's health. While high cycle fatigue degradation can be trended, a step-change in component health presents a challenge. This paper describes the development of two methods for step-change detection. These methods would allow the automated capture of maintenance or other events resulting in a step-change in component health. [ABSTRACT FROM AUTHOR]

Econometricians are increasingly working with high-dimensional networks and their dynamics. Econometricians, however, are often confronted with unforeseen changes in network dynamics. In this article, we develop a method and the corresponding algorithm for monitoring changes in dynamic networks. We characterize two types of changes, edge-initiated and node-initiated, to feature the complexity of networks. The proposed approach accounts for three potential challenges in the analysis of networks. First, networks are high-dimensional objects causing the standard statistical tools to suffer from the curse of dimensionality. Second, any potential changes in social networks are likely driven by a few nodes or edges in the network. Third, in many dynamic network applications such as monitoring network connectedness or its centrality, it will be more practically applicable to detect the change in an online fashion than the offline version. The proposed detection method at each time point projects the entire network onto a low-dimensional vector by taking the sparsity into account, then sequentially detects the change by comparing consecutive estimates of the optimal projection direction. As long as the change is sizeable and persistent, the projected vectors will converge to the optimal one, leading to a jump in the sine angle distance between them. A change is therefore declared. Strong theoretical guarantees on both the false alarm rate and detection delays are derived in a sub-Gaussian setting, even under spatial and temporal dependence in the data stream. Numerical studies and an application to the social media messages network support the effectiveness of our method. [ABSTRACT FROM AUTHOR]

We propose a new functional change point detection procedure, motivated by recent models for commodity futures term structure. We investigate our procedure's properties under the null hypothesis of no change and the alternative. Monte Carlo simulations reveal a reasonable power property in finite sample sizes although size distortion persists. An empirical analysis of oil futures markets identifies two change points near the 2008 financial crisis and 2020 crude oil negative territory. Regression models show that the price behaviour, in general, is exposed to the spot market index and exchange rate from 2007 to 2009. The main drivers of the price term structure are attributed to the trading activities of speculators and financial index innovations between 2017 and 2022. [ABSTRACT FROM AUTHOR]

The CUSUM (C) and dual C (DC) charts are well-known because of their sensitive nature against small-to-moderate shifts that occur in the process parameter(s). In this study, we propose new auxiliary information based (AIB) control charts for monitoring the process mean, which include Brownian motion-based C (BC), new BC (NBC), NBC with Crosier C (NBCC), dual NBC (DNBC) and dual NBCC (DNBCC) charts. In addition, fast initial response feature is also added to the proposed charts. We use Monte Carlo simulations to compute the run length properties of the control charts. Based on detailed run length comparisons, it is found that the proposed AIB charts are uniformly better than the existing AIB charts in terms of expected weighted and relative average run length benchmarks. An important concern all around the world is the quality and safety of drinking water. Several water quality tests are available that provide required information about the health of the waterway. Here, a water quality test dataset is taken on two variables, namely pH and total solids, and the proposed AIB charts are used to monitor the mean level of pH in the water. [ABSTRACT FROM AUTHOR]

Introduction: Applying transumbilical laparoendoscopic single-site surgery to endometrial cancers is worldwide, and the depiction of the learning curve is rarely described, which leads to the vagueness of young clinical practitioners. We accumulated the data to identify the completion of the learning curve by analyzing the operative and postoperative outcomes of the patients with endometrial cancer for transumbilical laparoendoscopic single-site surgery (TU-LESS). Methods: This was a retrospective, consecutive single-center study of patients with endometrial cancer undergoing standard endometrial cancer comprehensive staging surgery (extrafascial hysterectomy, bilateral salpingectomy, and pelvic lymphadenectomy) through TU-LESS by an experienced surgeon from December, 2017 to June, 2021 in the Department of Gynecologic Oncology, West China Second Hospital, Sichuan University, China. Results: After applying the inclusion and exclusion criteria, 42 patients were included in the study. The learning curve for this study was evaluated using both cumulative sum (CUSUM) and risk-adjusted CUSUM (RA-CUSUM) methods. Applying CUSUM and RA-CUSUM has grouped 42 cases into three phases. The prior five cases represented the learning period. The following six cases were needed to lay a technical foundation (cases 6-11). The third phase was regarded as achieving proficiency (cases 12-42). The operative time decreased drastically with the learning curve. There were no significant differences in terms of postoperative complications and lymph node retrieval among the three phases. More difficult patients were confronted in the third phase. Discussion: In our study, the learning curve was composed of three phases. According to the results of our study, 11 cases were required for experienced surgeons to achieve a technical foundation. [ABSTRACT FROM AUTHOR]

Most of the control charts in the literature focus on detecting constant mean shifts. In practice, dynamic and time-varying process mean shifts are prevalent in the monitoring of feedback-control and autocorrelated processes. The cumulative score (CUSCORE) charts have been proposed in the literature to detect dynamic and time-varying process shifts. One of such control charts is a reference-free CUSCORE (called RFCS-I) chart that may detect unknown patterned mean shifts effectively. In this paper, single and dual CUSCORE charts are developed to enable an efficient detection of unknown patterned mean shifts. The developed single CUSCORE chart, called the RFCS-II chart, is based on the control charting structure of the Crosier CUSUM (called CU-II) chart. Additionally, two RFCS-I charts are integrated with reflecting boundaries, while an integration of two RFCS-II charts is also made, to develop the dual RFCS-I (DRFCS-I) and dual RFCS-II (DRFCS-II) charts, respectively. In addition, the RFCS-I and RFCS-II charts are integrated to propose the mixed dual reference-free CUSCORE chart, called the MDRFCS chart. In essence, four CUSCORE charts, i.e. the RFCS-II, DRFCS-I, DRFCS-II and MDRFCS charts are suggested in this paper. The Monte Carlo simulation and a real application illustrate the superiority of these developed CUSCORE charts over their existing counterparts when detecting patterned mean shifts in terms of zero-state and steady-state relative mean indexes. [ABSTRACT FROM AUTHOR]

AbstractThis article presents a distribution-free image monitoring procedure for a manufacturing process, where a series of images are converted into a vector of two feature values extracted from singular value decomposition. Traditional image-based monitoring methods often make specific assumptions about marginal distributions and spatio-temporal dependence structures, which are often violated in real-world scenarios such as battery coating processes. To overcome this issue, we propose a distribution-free image monitoring procedure that detects a shift in the mean matrix of monitored images. Our method involves performing singular value decomposition of each image matrix in two ways to obtain two values, which are then combined into a bivariate vector. The bivariate vectors are monitored using a distribution-free multivariate CUSUM procedure, for which we determine control limits analytically, enabling convenient and easy implementation of the monitoring procedure. We demonstrate the effectiveness of our proposed procedure, as measured by average run lengths, using various simulated data and a real-data example from a battery coating process. [ABSTRACT FROM AUTHOR]

With the advancement of new energy grid integration, the scale of wind power installations has been expanding annually. Affected by regional weather changes, the intermittency and fluctuation characteristics of wind turbine output pose an increasingly significant threat to the power grid. Extreme weather-induced wind power output anomalies, known as ramp-up events, can lead to power grid imbalances and place tremendous pressure on power system unit dispatch and load balancing. Reasonable detection of wind power ramp-up events and accurate wind power forecasting can provide prior guidance for wind farm operation and maintenance and power system dispatch, effectively alleviating the harm caused by wind power uncertainty. Firstly, the blind spots in the current mainstream definitions of wind power ramp-up events are discussed. Following this, the power change characteristics of three wind power ramp-up scenarios were classified and analyzed. Then, a method for detecting wind power ramp-up events based on sliding window bilateral Cumulative Sum(CUSUM) was proposed. This method extracts time series coupling information, captures abnormal fluctuations in wind power data within a short time window, and improves the accuracy of wind power ramp-up event detection. Furthermore, a long short-term memory (LSTM) neural network optimized by Bayesian optimization was employed to optimize model hyperparameters and improve the model􀆳s predictive performance for wind turbine output during ramp-up events. The proposed wind power ramp-up event detection method was further applied to detect wind power ramp-up events within the model prediction interval, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Simple Summary: There have been few studies comparing the perioperative outcomes and learning curves of the operative time in pulmonary segmentectomy between uVATS and RATS performed by the same single surgeon, although both approaches are emerging as minimally invasive techniques for major lung resections worldwide. This study revealed that a surgeon reached the proficiency period earlier in RATS than in uVATS, although the trends to the stable period were similar. In contrast, most perioperative outcomes were statistically similar between the two approaches. When a surgeon begins each approach in pulmonary segmentectomy, the difference in operative time trend should be considered to achieve successful implementation. Background: The aim of this retrospective study was to compare the learning curve and perioperative outcomes between the two approaches uVATS and RATS during their implementation periods. Methods: The uVATS group included 77 consecutive uVATS segmentectomies performed by HI between February 2019 and June 2022, while the RATS group included 30 between July 2022 and September 2023. The patient characteristics, perioperative outcomes, and learning curves were compared between the two groups. The learning curve was evaluated using operative time and cumulative sum (CUSUMOT) analysis. Results: Most patient characteristics and perioperative outcomes were equivalent between the two groups. In the uVATS group, after a positive slope was observed until the 14th case (initial period), a plateau was observed until the 38th case (stable period). Finally, a negative slope was observed after the 38th case (proficiency period). In the RATS group, after a positive slope was observed until the 16th case (initial period), a plateau was observed until the 22nd case (stable period). Finally, a negative slope was observed after the 22nd case (proficiency period). Conclusions: In segmentectomy, a surgeon reached the proficiency period earlier in RATS than in uVATS, although the trends to the stable period were similar. [ABSTRACT FROM AUTHOR]

Rapidly detecting problems in the quality of care is of utmost importance for the well-being of patients. Without proper inspection schemes, such problems can go undetected for years. Cumulative sum (CUSUM) charts have proven to be useful for quality control, yet available methodology for survival outcomes is limited. The few available continuous time inspection charts usually require the researcher to specify an expected increase in the failure rate in advance, thereby requiring prior knowledge about the problem at hand. Misspecifying parameters can lead to false positive alerts and large detection delays. To solve this problem, we take a more general approach to derive the new Continuous time Generalized Rapid response CUSUM (CGR-CUSUM) chart. We find an expression for the approximate average run length (average time to detection) and illustrate the possible gain in detection speed by using the CGR-CUSUM over other commonly used monitoring schemes on a real-life data set from the Dutch Arthroplasty Register as well as in simulation studies. Besides the inspection of medical procedures, the CGR-CUSUM can also be used for other real-time inspection schemes such as industrial production lines and quality control of services. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to evaluate the learning curve of thoracoscopic repair of tracheoesophageal fistula (TEF) by a single surgeon using a cumulative sum (CUSUM) analysis. Methods: Prospective clinical data of consecutive Gross type-C TEF repairs performed by a pediatric surgeon from 2010 to 2020 were recorded. CUSUM charts for anastomosis and operating times were generated. The learning curves were compared with the effect of accumulation based on case experience. Results: For 33 consecutive cases, the mean operative and anastomosis times were 139 ± 39 min and 3137 ± 1110 s, respectively. Significant transitions beyond the learning phase for total operating and anastomosis times were observed at cases 13 and 17. Both the total operating time and anastomosis time were significantly faster in the proficiency improvement phase than in the initial learning phase. Postoperative complications significantly decreased after the initial anastomosis learning phase but not after the initial total operating learning phase. Conclusions: Thoracoscopic repair of TEF is considered safe and feasible after 13 cases, where the surgeon can improve their proficiency with the total operation procedure, and 17 cases, which will enable the surgeon to achieve proficiency in anastomosis. Postoperative complications significantly decreased after gaining familiarity with the anastomosis procedure through the learning phase. [ABSTRACT FROM AUTHOR]

As a novel procedure becomes more and more used, knowledge about its learning curve and its impact on outcomes is useful for future implementations. Our aim is (i) to identify the phases of the robotic rectal surgery learning process and assess the safety and oncological outcomes during that period, (ii) to compare the robotic rectal surgery learning phases outcomes with laparoscopic rectal resections performed before the implementation of the robotic surgery program. We performed a retrospective study, based on a prospectively maintained database, with methodological quality assessment by STROBE checklist. All the procedures were performed by the same two surgeons. A total of 157 robotic rectal resections from June 2018 to January 2022 and 97 laparoscopic rectal resections from January 2018 to July 2019 were included. The learning phase was completed at case 26 for surgeon A, 36 for surgeon B, and 60 for the center (both A & B). There were no differences in histopathological results or postoperative complications between phases, achieving the same ratio of mesorectal quality, circumferential and distal resection margins as the laparoscopic approach. A transitory increase of major complications and anastomotic leakage could occur once overcoming the learning phase, secondary to the progressive complexity of cases. Robotic rectal cancer surgery learning curve phases in experienced laparoscopic surgeons was completed after 25–35 cases. Implementation of a robotic rectal surgery program is safe in oncologic terms, morbidity, mortality and length of stay. [ABSTRACT FROM AUTHOR]

Process monitoring of multivariate quality attributes is important in many industrial applications, in which rich historical data are often available thanks to modern sensing technologies. While multivariate statistical process control (SPC) has been receiving increasing attention, existing methods are often inadequate as they are sensitive to the parametric model assumptions of multivariate data. In this paper, we propose a novel, nonparametric k-nearest neighbours empirical cumulative sum (KNN-ECUSUM) control chart that is a machine-learning-based black-box control chart for monitoring multivariate data by utilising extensive historical data under both in-control and out-of-control scenarios. Our proposed method utilises the k-nearest neighbours (KNN) algorithm for dimension reduction to transform multivariate data into univariate data and then applies the CUSUM procedure to monitor the change on the empirical distribution of the transformed univariate data. Extensive simulation studies and a real industrial example based on a disk monitoring system demonstrate the robustness and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Examinar las alteraciones en la oferta hídrica asociadas a la variabilidad climática, es de gran importancia para ajustar adaptaciones frente a los cambios actuales y futuros, para esto se requiere comprender cambios históricos espacio-temporales en el patrón regional de lluvias. Es por ello que, se analizaron las tendencias y la variabilidad de la precipitación (1980-2015) en escalas de la lluvia anual y mensual; mediante la aplicación de las pruebas no-paramétricas de Mann-Kendall y Cusum en 20 estaciones pluviométricas, pertenecientes de las cuencas de los ríos Esmeraldas, Mira y Cañar ubicada al extremo nor-oeste del territorio ecuatoriano. En los resultados, la precipitación anual tuvo tendencias significativas en el test EMK en la estación (M0001) y saltos en la media anual para las estaciones (M0301 y M0345) aplicando el test de cusum, la precipitación mensual no presento significancia para ninguno de los 2 test aplicados, con respecto a la variabilidad se encontró que la mayoría de las estaciones presentan una mayor precipitación a mayor altitud esto se debe a el efecto Foëhn siendo este uno de los factores más importantes que determinan las características climáticas las cuencas, concentrándose las mayores precipitaciones en las regiones de mayor altitud.

The choice of the factorization rank of a matrix is critical, e.g., in dimensionality reduction, filtering, clustering, deconvolution, etc., because selecting a rank that is too high amounts to adjusting the noise, while selecting a rank that is too low results in the oversimplification of the signal. Numerous methods for selecting the factorization rank of a non-negative matrix have been proposed. One of them is the cophenetic correlation coefficient ( c c c ), widely used in data science to evaluate the number of clusters in a hierarchical clustering. In previous work, it was shown that c c c performs better than other methods for rank selection in non-negative matrix factorization (NMF) when the underlying structure of the matrix consists of orthogonal clusters. In this article, we show that using the ratio of c c c to the approximation error significantly improves the accuracy of the rank selection. We also propose a new criterion, c o n c o r d a n c e , which, like c c c , benefits from the stochastic nature of NMF; its accuracy is also improved by using its ratio-to-error form. Using real and simulated data, we show that c o n c o r d a n c e , with a CUSUM-based automatic detection algorithm for its original or ratio-to-error forms, significantly outperforms c c c . It is important to note that the new criterion works for a broader class of matrices, where the underlying clusters are not assumed to be orthogonal. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we propose an adaptive top-r method to monitor large-scale data streams where the change may affect a set of unknown data streams at some unknown time. Motivated by parallel and distributed computing, we propose to develop global monitoring schemes by parallel running local detection procedures and then use the Benjamin-Hochberg (BH) false discovery rate (FDR) control procedure to estimate the number of changed data streams adaptively. Our approach is illustrated in two concrete examples: one is a homogeneous case when all data streams are i.i.d with the same known pre-change and post-change distributions. The other is when all data are normally distributed, and the mean shifts are unknown and can be positive or negative. Theoretically, we show that when the pre-change and post-change distributions are completely specified, our proposed method can estimate the number of changed data streams for both the pre-change and post-change status. Moreover, we perform simulations and two case studies to show its detection efficiency. [ABSTRACT FROM AUTHOR]

Background: A program of gasless laparoscopy (GL) has been implemented in rural North-East India. To facilitate safe adoption, participating rural surgeons underwent rigorous training prior to independent clinical practice. An online registry was established to capture clinical data on safety and efficacy and to evaluate initial learning curves for gasless laparoscopy. Methods: Surgeons who had completed the GL training program participated in the online RedCap Registry. Patients included in the registry provided informed consent for the use of their data. Data on operative times, conversion rates, perioperative complications, length of stay, and hospital costs were collected. Fixed reference cumulative sum (CUSUM) model was used to evaluate the learning curve based on operative times and conversion rates published in the literature. Results: Four surgeons from three rural hospitals in North-East India participated in the registry. The data were collected over 12 months, from September 2019 to August 2020. One hundred and twenty-three participants underwent GL procedures, including 109 females (88.6%) and 14 males. GL procedures included cholecystectomy, appendicectomy, tubal ligation, ovarian cystectomy, diagnostic laparoscopy, and adhesiolysis. The mean operative time was 75.3 (42.05) minutes for all the surgeries. Conversion from GL to open surgery occurred in 11.4% of participants, with 8.9% converted to conventional laparoscopy. The main reasons for conversion were the inability to secure an operative view, lack of operating space, and adhesions. The mean length of stay was 3 (2.1) days. The complication rate was 5.7%, with one postoperative death. The CUSUM analysis for GL cholecystectomy showed a longer learning curve for operative time and few conversions. The learning curve for GL tubal ligation was relatively shorter. Conclusion: Gasless laparoscopy can be safely implemented in the rural settings of Northeast India with appropriate training programs. Careful case selection is essential during the early stages of the surgical learning curve. [ABSTRACT FROM AUTHOR]

Study Design. Retrospective review of prospectively collected data. Objective. To analyze the learning curves of three spine surgeons for robotic minimally invasive transforaminal lumbar interbody fusion (MI-TLIF). Summary of Background Data. Although the learning curve for robotic MI-TLIF has been described, the current evidence is of low quality with most studies being single-surgeon series. Materials and Methods. Patients who underwent single-level MI-TLIF with three spine surgeons (years in practice: surgeon 1: 4, surgeon 2: 16, and surgeon 3: two) using a floor-mounted robot were included. Outcome measures were operative time, fluoroscopy time, intraoperative complications, screw revision, and patientreported outcome measures. Each surgeon's cases were divided into successive groups of 10 patients and compared for differences. Linear regression and cumulative sum (CuSum) analyses were performed to analyze the trend and learning curve, respectively. Results. A total of 187 patients were included (surgeon 1: 45, surgeon 2: 122, and surgeon 3: 20). For surgeon 1, CuSum analysis showed a learning curve of 21 cases with the attainment of mastery at case 31. Linear regression plots showed negative slopes for operative and fluoroscopy time. Both learning phase and postlearning phase groups showed significant improvement in patient-reported outcome measures. For surgeon 2, CuSum analysis demonstrated no discernible learning curve. There was no significant difference between successive patient groups in either operative time or fluoroscopy time. For surgeon 3, CuSum analysis demonstrated no discernible learning curve. Even though the difference between successive patient groups was not significant, cases 11 to 20 had an average operative time of 26 minutes less than cases 1-10), suggesting an ongoing learning curve. Conclusions. Surgeons who are well-experienced can be expected to have no or minimal learning curve for robotic MI-TLIF. Early attendings are likely to have a learning curve of around 21 cases with the attainment of mastery at case 31. Learning curve does not seem to impact clinical outcomes after surgery. [ABSTRACT FROM AUTHOR]

Count data occur widely in many bio-surveillance and healthcare applications, e.g. the numbers of new patients of different types of infectious diseases from different cities/counties/states repeatedly over time, say, daily/weekly/monthly. For this type of count data, one important task is the quick detection and localization of hot-spots in terms of unusual infectious rates so that we can respond appropriately. In this paper, we develop a method called Poisson assisted Smooth Sparse Tensor Decomposition (PoSSTenD), which not only detect when hot-spots occur but also localize where hot-spots occur. The main idea of our proposed PoSSTenD method is articulated as follows. First, we represent the observed count data as a three-dimensional tensor including (1) a spatial dimension for location patterns, e.g. different cities/countries/states; (2) a temporal domain for time patterns, e.g. daily/weekly/monthly; (3) a categorical dimension for different types of data sources, e.g. different types of diseases. Second, we fit this tensor into a Poisson regression model, and then we further decompose the infectious rate into two components: smooth global trend and local hot-spots. Third, we detect when hot-spots occur by building a cumulative sum (CUSUM) control chart and localize where hot-spots occur by their LASSO-type sparse estimation. The usefulness of our proposed methodology is validated through numerical simulation studies and a real-world dataset, which records the annual number of 10 different infectious diseases from 1993 to 2018 for 49 mainland states in the United States. [ABSTRACT FROM AUTHOR]

As a result of the severe energy shortage and the greenhouse effect, experts worldwide have been devoted to solving energy management problems. Smart grid construction is an essential technology for mastering energy allocation. Smart grids enable end users to adjust their energy consumption via incentive measures, reduce the frequency of power supply instability, and improve energy efficiency. Non-intrusive load monitoring (NILM) is a vital technology for smart grid construction. One of the fundamental steps of NILM is event detection. Proper event detection can increase the accuracy of load identification. Among traditional methods, especially the event detection method developed with the CUSUM method, although the accuracy is reasonable, the precision, recall, and f1 score are not relatively better. Thus, there is an opportunity to improve the performance of CUSUM. Additionally, many studies focus on the step-like event, but the long-transient event is often overlooked in event detection. Therefore, in this study, it was observed that when the transient current deviates from the steady-state current, the transient current can be regarded as a key indicator for event detection. With this observation, a method is proposed to convert the root mean square (RMS) current into a cumulative sum (CUSUM) diagram method and identify turning points representing events from the CUSUM geometry. Once the slope of the turning point has been determined, event detection is achieved. Compared with traditional methods, the proposed method is easy to implement, its recognition rate can reach around 98%, and the window length is reduced from 5 s to 3 s. [ABSTRACT FROM AUTHOR]

Monitoring of the process coefficient of variation (CV) with one‐sided CUSUM charts has been considered in the literature. In this short note, we develop a near‐optimal one‐sided CUSUM chart (with an explicit mathematical expression of the reference parameter) for detecting either increases or decreases in the CV of a normally distributed process. It is shown that the run‐length profiles of the existing and proposed one‐sided CUSUM‐CV charts (despite the fact that both charts possess different control‐charting structures) approximately remain the same. However, the proposed one‐sided CUSUM‐CV charts can be easily optimized when detecting a specific upward or downward shift in the in‐control process CV. [ABSTRACT FROM AUTHOR]

Introduction: There has been increased interest in assessing the surgeon learning curve for new skill acquisition. While there is no consensus around the best methodology, one of the most frequently used learning curve assessments in the surgical literature is the cumulative sum curve (CUSUM) of operative time. To demonstrate the limitations of this methodology, we assessed the CUSUM of console time across cohorts of surgeons with differing case acquisition rates while varying the total number of cases used to calculate the CUSUM. Methods: We compared the CUSUM curves of the average console times of surgeons who completed their first 20 robotic-assisted (RAS) cases in 13, 26, 39, and 52 weeks, respectively, for their first 50 and 100 cases, respectively. This analysis was performed for prostatectomy (1094 surgeons), malignant hysterectomy (737 surgeons), and inguinal hernia (1486 surgeons). Results: In all procedures, the CUSUM curve of the cohort of surgeons who completed their first 20 procedures in 13 weeks demonstrated a lower slope than cohorts of surgeons with slower case acquisition rates. The case number at which the peak of the CUSUM curve occurs uniformly increases when the total number of cases used in generation of the CUSUM chart changes from 50 to 100 cases. Conclusion: The CUSUM analyses of these three procedures suggests that surgeons with fast initial case acquisition rates have less variability in their operative times over the course of their learning curve. The peak of the CUSUM curve, which is often used in surgical learning curve literature to denote "proficiency" is predictably influenced by the total number of procedures evaluated, suggesting that defining the peak as the point at which a surgeon has overcome the learning curve is subject to routine bias. The CUSUM peak, by itself, is an insufficient measure of "conquering the learning curve." [ABSTRACT FROM AUTHOR]

The control chart is a fundamental tool in statistical process control (SPC), widely employed in manufacturing and construction industries for process monitoring with the primary objective of maintaining quality standards and improving operational efficiency. Control charts play a crucial role in identifying special cause variations and guiding the process back to statistical control. While Shewhart control charts excel at detecting significant shifts, EWMA and CUSUM charts are better suited for detecting smaller to moderate shifts. However, the effectiveness of all these control charts is compromised when the underlying distribution deviates from normality. In response to this challenge, this study introduces a robust mixed EWMA-CUSUM control chart tailored for monitoring processes characterized via symmetric but non-normal distributions. The key innovation of the proposed approach lies in the integration of a robust estimator, based on order statistics, that leverages the generalized least square (GLS) technique developed by Lloyd. This integration enhances the chart's robustness and minimizes estimator variance, even in the presence of non-normality. To demonstrate the effectiveness of the proposed control chart, a comprehensive comparison is conducted with several well-known control charts. Results of the study clearly show that the proposed chart exhibits superior sensitivity to small and moderate shifts in process parameters when compared to its predecessors. Through a compelling illustrative example, a real-life application of the enhanced performance of the proposed control chart is provided in comparison to existing alternatives. [ABSTRACT FROM AUTHOR]

Robot-assisted surgery has been proven to offer improvements in term of surgical learning curve and feasibility of minimally invasive surgery, but has often been criticized for its longer operative times compared to conventional laparoscopy. Additional times can be split into time required for system set-up, robotic arms docking and calibration of robotic instruments; secondly, surgeon's learning curve. One of the newest systems recently launched on the market is the Hugo™ RAS (MEDTRONIC Inc, United States). As some of the earliest adopters of the Hugo™ RAS system technology, we present our data on robotic docking learning curve for the first 192 gynecologic robotic cases performed at our institution. Our data indicates that robotic set-up and docking with the new Hugo™ RAS robotic surgical system can be performed time-effectively and that the specific robotic docking learning curve is comparable to preexisting data for other platforms. This preliminary insights into this recently released system may be worthwhile for other centers which may soon adopt this new technology and may need some relevant information on topics such as OR times. Further studies are necessary to assess the different features of the Hugo™ RAS considering other technical and surgical aspects, to fully become familiar with this novel technology. [ABSTRACT FROM AUTHOR]

Non-parametric control charts have been good alternatives to parametric control charts when little information is known about the type of process distribution or the value of parameters. Most approaches proposed by the current literature monitor either location or scale change in batch mode and their performance is discounted when monitoring distribution change in both location and scale simultaneously in a sequential pattern. This paper proposed a log-likelihood-ratio-based non-parametric cumulative sum (CUSUM) control chart to monitor arbitrary distribution change and diagnose the detailed change type simultaneously. By integrating the superiority of log-likelihood ratio test to detect any change of distribution and CUSUM chart to detect a small change, the proposed approach can detect small potential changes in location, scale and shape; and provide detailed information about change type when control chart gives a signal. Comparison results with many other non-parametric approaches were provided by numerical simulation and the results of an application case demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

In order to solve the problem of locomotive failure caused by the fault of grid-side grounding due to misinformation of grid-side current signal in the application of AC locomotive, a real-time diagnosis method for grid-side current signal anomalies based on multi-operating condition nonlinear model is proposed in this paper. Firstly, based on the system mechanism and engineering design experience, the variables strongly related to grid-side current are extracted, and a multi-operating condition nonlinear estimation model of grid-side current is established. Secondly, based on the two redundant grid-side current signals of the traction drive system and the estimated value of the grid-side current, residual and detection statistics were designed, and the effective fault detection and decision-making of the two grid-side current signal anomalies are achieved by combining the CUSUM algorithm. Finally, different fault-tolerant control strategies are adopted according to the fault decision-making results to ensure the normal control and protection functions of the system. Field data tests show that the proposed diagnostic method achieves timely detection and precise positioning of grid-side anomalies 0.3 s after the occurrence of anomalies, avoiding the impact of on-site faults caused by grid-side signal anomalies.

Abstract Objective Compare and analyze clinical data of total laparoscopic pancreaticoduodenectomy (TLPD) cases for surgeons with / without first assistant experience (FAE) in TLPD. Probe influence of FAE in TLPD on the learning curve for an operator. Methods The clinical data of 239 patients, that underwent TLPD performed by two surgeons between January 2017 and January 2022) in our department, were consecutively collected and divided into two groups (A and B). Group A cases were operated by Surgeon A, with FAE of 57 TLPDs in our department prior to initial TLPD as an operator. Group B cases were operated by Surgeon B with no FAE of TLPD. Cumulative sum (CUSUM) method developed learning curves. Clinical data and both surgeons’ learning curves were statistically compared between both groups. Results Between both groups, no statistically significant variations were observed for pre-operative health conditions. Reduced surgical duration, blood loss and transfusion volume during surgery, together with reductions in major post–operative complication rates and reduced hospital/ICU stays were identified within Group A, having statistically significant variations. The technical plateau phases of the learning curves were approximately 25–41 cases and 35–51 cases, for Surgeon A and Surgeon B, respectively. Conclusion FAE in TLPD can accelerate the learning curve of TLPD for an operator, with safer surgical procedures and enhanced post–operative recovery.

Monitoring techniques based on the multivariate coefficients of variation (MCV) have received a great deal of attention in quality control. Numerous studies have shown that adaptively changing the charting parameters based on the past sample information can improve the performance of a chart in detecting process changes. In view of the performance benefits of adaptive strategies, two one-sided cumulative sum (CUSUM) charts are proposed to monitor the MCV and a variable sampling interval (VSI) strategy is incorporated into these charts. By using the Markov chain method, the formulas for calculating the average and standard deviation of time to signal measures of the VSI CUSUM MCV control charts are derived. Then for the known and unknown shift sizes, the optimization algorithms for obtaining the charting parameters are presented. The superiority of the proposed control charts is confirmed by comparing their performance with two existing MCV charts. Finally, an example using real investment data demonstrates the practical application of the proposed charts.

In the context of big data, multivariate processes must often be monitored in a timely and accurate manner. Usually, the distribution of process variables is unknown. This paper proposes a new strategy for multivariate process monitoring when the distribution of a process variable is unknown. We address monitoring by means of a rank-based method that is completely nonparametric. We also discuss the optimal strategy of parameters. A simulation study demonstrates that the proposed method is efficient in detecting shifts for multivariate processes. A real data example is presented to illustrate the performance of the proposed method. [ABSTRACT FROM AUTHOR]

Two methods of structural break detection in a piecewise generalized model of autoregressive conditional heteroscedasticity are considered. The first method is based on Kolmogorov–Smirnov statistics and is called the KS method. The second one is based on the cumulative sums and is called the KL method. In this paper, the KS and KL methods are compared under the assumption of Student's conditional distribution of random errors. The results of our Monte Carlo experiments are as follows: the KL method is inferior to the KS method both in terms of the average probability of errors of the first type and in terms of the average power of detecting a structural break. [ABSTRACT FROM AUTHOR]

This article studies multiple structural breaks in large contemporaneous covariance matrices of high-dimensional time series satisfying an approximate factor model. The breaks in the second-order moment structure of the common components are due to sudden changes in either factor loadings or covariance of latent factors, requiring appropriate transformation of the factor models to facilitate estimation of the (transformed) common factors and factor loadings via the classical principal component analysis. With the estimated factors and idiosyncratic errors, an easy-to-implement CUSUM-based detection technique is introduced to consistently estimate the location and number of breaks and correctly identify whether they originate in the common or idiosyncratic error components. The algorithms of Wild Binary Segmentation for Covariance (WBS-Cov) and Wild Sparsified Binary Segmentation for Covariance (WSBS-Cov) are used to estimate breaks in the common and idiosyncratic error components, respectively. Under some technical conditions, the asymptotic properties of the proposed methodology are derived with near-optimal rates (up to a logarithmic factor) achieved for the estimated breaks. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the developed method and its comparison with other existing approaches. We finally apply our method to study the contemporaneous covariance structure of daily returns of S&P 500 constituents and identify a few breaks including those occurring during the 2007–2008 financial crisis and the recent coronavirus (COVID-19) outbreak. An R package " BSCOV " is provided to implement the proposed algorithms. [ABSTRACT FROM AUTHOR]