Detection of Multiple Structural Breaks in Large Covariance Matrices

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 package "” is provided to implement the proposed algorithms.

The implication of pollution control acts & protocols and COVID-19 on air quality: A case study of Mumbai, India

This study aims to explore twin objectives. Initially, the study scrutinises the consequences of various pollution control acts and protocols signed by India to improve the air quality and then the study involves itself to investigate the aftermath of COVID-19 lockdown on the air quality of highly populated Mumbai city of India. The empirical analysis is facilitated by the application of Poirier's Spline function approach on the secondary data compiled from the Maharashtra Pollution Control Board (MPCB). The corresponding structural shifting points are identified through the CUSUM of squares (CUSUMQ) test. The empirical results disclose that Kyoto Protocol and lockdown have positively influenced the air quality. This study ends with suitable policy prescriptions. © 2023 by Shrabanti Maity, Ummey Rummana Barlaskar and Nandini Ghosh.

A Fast Initial Response Approach to Sequential Financial Surveillance

Consider the problem of financial surveillance of a heavy-tailed time series modeled as a geometric random walk with log-Student's t increments assuming a constant volatility. Our proposed sequential testing method is based on applying the recently developed taut string (TS) univariate process monitoring scheme to the gaussianized log-differenced process data. With the signal process given by a properly scaled total variation norm of the nonparametric taut string estimator applied to the gaussianized log-differences, the change point detection procedure is constructed to have a desired in-control (IC) average run length (ARL) assuming no change in the process drift. If a change in the process drift is imminent, the proposed approach offers an effective fast initial response (FIR) instrument for rapid yet reliable change point detection. This framework may be particularly advantageous for protection against imminent upsets in financial time series in a turbulent socioeconomic and/or political environment. We illustrate how the proposed approach can be applied to sequential surveillance of real-world financial data originating from Meta Platforms, Inc. (FB) stock prices and compare the performance of the TS chart to that of the more prominent CUSUM and CUSUM FIR charts at flagging the COVID-19 related crash of February 2020. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

A CUSUM test for change point in quantile regression for longitudinal data

In regression analysis a single parametric form is assumed, over the whole domain of interest. However, this assumption might not be valid in some applications, such as existence of a change point in the functional form. In this case we need to detect and estimate such change point. Also, it is common to assume normality of the response variable when dealing with the change point problem. The normality assumption can be violated in many cases, such as heavy-tailed data or in the presence of outliers. In such cases, the quantile regression is a good candidate. It is known that the quantile regression is distribution free and robust to outliers. The CUSUM test has been used to detect the existence of a threshold effect (change point) to the quantile regression model in cross-sectional data. This article proposes and develops the CUSUM test, in longitudinal data setting, to investigate the existence of a change point in quantile regression model. Simulation study is used to assess the performance of the proposed test. Finally, the proposed test is used to detect any possible change points in a COVID-19 data. [ FROM AUTHOR] Copyright of Communications in Statistics: Simulation & Computation is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Adaptive resources allocation CUSUM for binomial count data monitoring with application to COVID-19 hotspot detection

In this paper, we present an efficient statistical method (denoted as 'Adaptive Resources Allocation CUSUM') to robustly and efficiently detect the hotspot with limited sampling resources. Our main idea is to combine the multi-arm bandit (MAB) and change-point detection methods to balance the exploration and exploitation of resource allocation for hotspot detection. Further, a Bayesian weighted update is used to update the posterior distribution of the infection rate. Then, the upper confidence bound (UCB) is used for resource allocation and planning. Finally, CUSUM monitoring statistics to detect the change point as well as the change location. For performance evaluation, we compare the performance of the proposed method with several benchmark methods in the literature and showed the proposed algorithm is able to achieve a lower detection delay and higher detection precision. Finally, this method is applied to hotspot detection in a real case study of county-level daily positive COVID-19 cases in Washington State WA) and demonstrates the effectiveness with very limited distributed samples.

Detection of Multiple Structural Breaks in Large Covariance Matrices

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. for this article are available online.

Research on the Invulnerability and Early Warning of Global Stock Market Networks Under the Background of Epidemic

The Covid-19 epidemic has a significant impact on the global economy. In this paper, the minimum spanning tree method and threshold method are combined to build a correlation network for three periods before the outbreak, domestic outbreak and global spread of the epidemic. By comparing the network topology, survivability and node importance in the three stages, it is found that the epidemic has significantly enhanced the linkage effect between global stock markets. The global stock correlation network has obvious small-world characteristics, and the betweenness of nodes obey power law distribution. In the simulation experiment of network attack, deliberate attack is more destructive than random attack, and the robustness of global stock network in three periods is enhanced successively. The importance of stocks changed significantly before and after the epidemic. Chinese mainland and Hong Kong were the first to suffer a huge impact in the epidemic, but they quickly adjusted, while Europe and The United States were only affected after the global epidemic worsened. In addition, the application of cumulative sum control chart (CUSUM) to the early warning of stock prices has also achieved good results. © 2022, The Editorial Department of Complex Systems and Complexity Science. All right reserved.

On the early detecting of the COVID-19 outbreak.

INTRODUCTION: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. METHODOLOGY: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. RESULTS: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak.

Bronchoscopist's perception of the quality of the single-use bronchoscope (Ambu aScope4™) in selected bronchoscopies: a multicenter study in 21 Spanish pulmonology services.

BACKGROUND: The disposable bronchoscope is an excellent alternative to face the problem of SARS-CoV-2 and other cross infections, but the bronchoscopist's perception of its quality has not been evaluated. METHODS: To evaluate the quality of the Ambu-aScope4 disposable bronchoscope, we carried out a cross-sectional study in 21 Spanish pulmonology services. We use a standardized questionnaire completed by the bronchoscopists at the end of each bronchoscopy. The variables were described with absolute and relative frequencies, measures of central tendency and dispersion depending on their nature. The existence of learning curves was evaluated by CUSUM analysis. RESULTS: The most frequent indications in 300 included bronchoscopies was bronchial aspiration in 69.3% and the median duration of these was 9.1 min. The route of entry was nasal in 47.2% and oral in 34.1%. The average score for ease of use, image, and aspiration quality was 80/100. All the planned techniques were performed in 94.9% and the bronchoscopist was satisfied in 96.6% of the bronchoscopies. They highlighted the portability and immediacy of the aScope4TM to start the procedure in 99.3%, the possibility of taking and storing images in 99.3%. The CUSUM analysis showed average scores > 70/100 from the first procedure and from the 9th procedure more than 80% of the scores exceeded the 80/100 score. CONCLUSIONS: The aScope4™ scored well for ease of use, imaging, and aspiration. We found a learning curve with excellent scores from the 9th procedure. Bronchoscopists highlighted its portability, immediacy of use and the possibility of taking and storing images.