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Detection of Multiple Structural Breaks in Large Covariance Matrices

JOURNAL OF BUSINESS & ECONOMIC STATISTICS 2023年第3期41卷 846-861页

作者： Li, Yu-Ning Li, Degui Fryzlewicz, Piotr Zhejiang Univ Sch Math Sci Hangzhou Peoples R China Univ York Dept Econ & Related Studies York N Yorkshire England Univ York Dept Math York YO10 5DD N Yorkshire England London Sch Econ Dept Stat London England

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.

关键词： Approximate factor models binary segmentation CUSUM Large covariance matrix Principal component analysis Structural breaks

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A Distributed Intrusion Detection Scheme for Cloud Computing

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INTERNATIONAL JOURNAL OF DISTRIBUTED SYSTEMS AND TECHNOLOGIES 2020年第1期11卷 68-82页

作者： Ibrahim, Nurudeen Mahmud Zainal, Anazida Univ Teknol Johor Baharu Malaysia

Intrusion detection systems (IDS) is an important security measure used to secure cloud resources, however, IDS often suffer from poor detection accuracy due to coordinated attacks such as a DDoS. Various research on distributed IDS s have been proposed to detect DDoS however, the limitations of these works the lack of technique to determine an appropriate period to share attack information among nodes in the distributed IDS. Therefore, this article proposes a distributed IDS that uses a binary segmentation change point detection algorithm to address the appropriate period to send attack information to nodes in distributed IDS and using parallel Stochastic Gradient Descent with Support Vector Machine (SGD-SVM) to achieve the distributed detection. The result of the proposed scheme was implemented in Apache Spark using NSL-KDD benchmark intrusion detection dataset. Experimental results show that the proposed distributed intrusion detection scheme outperforms existing distributed IDS for cloud computing.

关键词： Anomaly Detection binary segmentation Cloud security Distributed IDS Machine Learning Stochastic Gradient Descent DDoS

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Retinal image segmentation using double-scale non-linear thresholding on vessel support regions

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CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY 2017年第3期2卷 109-115页

作者： Li, Qingyong Zheng, Min Li, Feng Wang, Jianzhu Geng, Yangli-ao Jiang, Haibo Beijing Jiaotong Univ Beijing Key Lab Transportat Data Anal & Min Beijing 100044 Peoples R China Cent South Univ Xiangya Hosp Dept Ophthalmol Changsha 410083 Peoples R China

Retinal vessel segmentation is a critical indicator of diagnosis, screening, and treatment of cardiovascular and ophthalmologic diseases. Due to the fact that the retinal vessels usually have some tiny structures and blurred boundaries, especially with remarkable noises, it is difficult to correctly segment the vascular networks. In this study, the authors propose a double-scale non-linear thresholding method based on vessel support regions. First, the double-scale filtering method is applied to enhance the contrast between the foreground vascular and the background stuffs. Second, they segment the fine and coarse vessels by the corresponding adaptive local thresholding and fixed-ratio thresholding method. Finally, they obtain the binary segmentation by fusion of fine and coarse vessels. Experiments are conducted on the publicly available DRIVE and STARE datasets, which show the effectiveness of the proposed method on retinal vessel segmentation.

关键词： cardiovascular diseases contrast enhancement fixed-ratio thresholding method ophthalmologic diseases blood vessels image enhancement retinal image segmentation retinal vessels binary segmentation diseases eye coarse vessels adaptive local thresholding fine vessels vessel support regions double-scale filtering method image segmentation double-scale non-linear thresholding double-scale nonlinear thresholding method medical image processing retinal vessel segmentation

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Multiple Change-Point Detection: A Selective Overview

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STATISTICAL SCIENCE 2016年第4期31卷 611-623页

作者： Niu, Yue S. Hao, Ning Zhang, Heping Univ Arizona Dept Math 617 N Santa Rita Ave Tucson AZ 85721 USA Yale Sch Publ Hlth Biostat 60 Coll St New Haven CT 06520 USA

Very long and noisy sequence data arise from biological sciences to social science including high throughput data in genomics and stock prices in econometrics. Often such data are collected in order to identify and understand shifts in trends, for example, from a bull market to a bear market in finance or from a normal number of chromosome copies to an excessive number of chromosome copies in genetics. Thus, identifying multiple change points in a long, possibly very long, sequence is an important problem. In this article, we review both classical and new multiple change-point detection strategies. Considering the long history and the extensive literature on the change-point detection, we provide an in-depth discussion on a normal mean change-point model from aspects of regression analysis, hypothesis testing, consistency and inference. In particular, we present a strategy to gather and aggregate local information for change-point detection that has become the cornerstone of several emerging methods because of its attractiveness in both computational and theoretical properties.

关键词： binary segmentation consistency multiple testing normal mean change-point model regression screening and ranking algorithm

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Change-point detection in panel data via double CUSUM statistic

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ELECTRONIC JOURNAL OF STATISTICS 2016年第2期10卷 2000-2038页

作者： Cho, Haeran Univ Bristol Sch Math Bristol Avon England

In this paper, we consider the problem of (multiple) change-point detection in panel data. We propose the double CUSUM statistic which utilises the cross-sectional change-point structure by examining the cumulative sums of ordered CUSUMs at each point. The efficiency of the proposed change-point test is studied, which is reflected on the rate at which the cross-sectional size of a change is permitted to converge to zero while it is still detectable. Also, the consistency of the proposed change-point detection procedure based on the binary segmentation algorithm, is established in terms of both the total number and locations (in time) of the estimated change-points. Motivated by the representation properties of the Generalised Dynamic Factor Model, we propose a bootstrap procedure for test criterion selection, which accounts for both cross-sectional and within-series correlations in high-dimensional data. The empirical performance of the double CUSUM statistics, equipped with the proposed bootstrap scheme, is investigated in a comparative simulation study with the state-of-the-art. As an application, we analyse the log returns of S&P 100 component stock prices over a period of one year.

关键词： Change-point analysis high-dimensional data analysis CUSUM statistics binary segmentation

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Automated Road Extraction from Remotely Sensed Imagery using ConnectNet

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2023年第10期51卷 2105-2120页

作者： Das, Prativa Chand, Satish Singh, Nitin Kumar Singh, Pardeep Siksha OAnusandhan Univ Inst Tech Educ & Res Bhubaneswar 751019 Orissa India Jawaharlal Nehru Univ Sch Comp & Syst Sci Delhi 110067 India

Extracting road maps from high-resolution remotely sensed imagery has many practical applications: improving connectivity in remote areas, monitoring urban expansion, and providing aid to disaster-prone regions. Despite the abundance of satellite and aerial imagery, substantial obstacles such as opacity, shadows, inter-class similarity, and missing roadways persist, necessitating immediate attention from researchers. This paper proposes a memory-efficient end-to-end convolution neural network-based architecture called ConnectNet that exploits the powerful features of the Res2Net model. Multi-scale features within every residual block are captured by the hierarchical design, enhancing the proposed model's representation ability. A new block, Stacked Feature Fusion, is proposed having dilated convolution layers of different rates stacked with the squeeze and excitation blocks. Both long-range and narrow-range dependencies are captured by this block, minimizing the boundary and edge loss issue. A new loss function, collective loss, is introduced that combines dice coefficient, binary cross-entropy, and Lovasz sigmoid loss functions which further improves the convergence time and resolves the class imbalance issue. Extensive experiments have been conducted to demonstrate and compare the proposed model's results with other road extraction methods on two publicly available Massachusetts Road Dataset and SpaceNet 3 Road Network Detection Dataset. The quantitative and visual results show that the proposed model outperforms state-of-the-art methods by a significantly large margin.

关键词： Road extraction binary segmentation Aerial imagery Multi-scale features Deep learning CNN

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Multiple-change-point detection for auto-regressive conditional heteroscedastic processes

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2014年第5期76卷 903-924页

作者： Fryzlewicz, P. Rao, S. Subba Univ London London Sch Econ & Polit Sci London WC2A 2AE England Texas A&M Univ College Stn TX USA

The emergence of the recent financial crisis, during which markets frequently underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. Motivated by this observation, we propose a fast, well performing and theoretically tractable method for detecting multiple change points in the structure of an auto-regressive conditional heteroscedastic model for financial returns with piecewise constant parameter values. Our method, termed BASTA (binary segmentation for transformed auto-regressive conditional heteroscedasticity), proceeds in two stages: process transformation and binary segmentation. The process transformation decorrelates the original process and lightens its tails;the binary segmentation consistently estimates the change points. We propose and justify two particular transformations and use simulation to fine-tune their parameters as well as the threshold parameter for the binary segmentation stage. A comparative simulation study illustrates good performance in comparison with the state of the art, and the analysis of the Financial Times Stock Exchange FTSE 100 index reveals an interesting correspondence between the estimated change points and major events of the recent financial crisis. Although the method is easy to implement, ready-made R software is provided.

关键词： binary segmentation Cumulative sum Mixing Non-stationary time series Process transformation Unbalanced Haar wavelets

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Finite sample change point inference and identification for high-dimensional mean vectors

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY 2021年第2期83卷 247-270页

作者： Yu, Mengjia Chen, Xiaohui Univ Illinois Dept Stat 725 S Wright St Champaign IL 61820 USA

Cumulative sum (CUSUM) statistics are widely used in the change point inference and identification. For the problem of testing for existence of a change point in an independent sample generated from the mean-shift model, we introduce a Gaussian multiplier bootstrap to calibrate critical values of the CUSUM test statistics in high dimensions. The proposed bootstrap CUSUM test is fully data dependent and it has strong theoretical guarantees under arbitrary dependence structures and mild moment conditions. Specifically, we show that with a boundary removal parameter the bootstrap CUSUM test enjoys the uniform validity in size under the null and it achieves the minimax separation rate under the sparse alternatives when the dimension p can be larger than the sample size n. Once a change point is detected, we estimate the change point location by maximising the l infinity-norm of the generalised CUSUM statistics at two different weighting scales corresponding to covariance stationary and non-stationary CUSUM statistics. For both estimators, we derive their rates of convergence and show that dimension impacts the rates only through logarithmic factors, which implies that consistency of the CUSUM estimators is possible when p is much larger than n. In the presence of multiple change points, we propose a principled bootstrap-assisted binary segmentation (BABS) algorithm to dynamically adjust the change point detection rule and recursively estimate their locations. We derive its rate of convergence under suitable signal separation and strength conditions. The results derived in this paper are non-asymptotic and we provide extensive simulation studies to assess the finite sample performance. The empirical evidence shows an encouraging agreement with our theoretical results.

关键词： binary segmentation bootstrap CUSUM change point analysis Gaussian approximation high‐ dimensional data

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A computationally efficient, high-dimensional multiple changepoint procedure with application to global terrorism incidence

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JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY 2021年第4期184卷 1303-1325页

作者： Tickle, S. O. Eckley, I. A. Fearnhead, P. Univ Lancaster STOR I Ctr Doctoral Training Lancaster England Univ Lancaster Dept Math & Stat Lancaster England Univ Bristol Sch Math Fry Bldg Bristol BS8 1UG Avon England Heilbronn Inst Math Res Bristol Avon England

Detecting changepoints in data sets with many variates is a data science challenge of increasing importance. Motivated by the problem of detecting changes in the incidence of terrorism from a global terrorism database, we propose a novel approach to multiple changepoint detection in multivariate time series. Our method, which we call SUBSET, is a model-based approach which uses a penalised likelihood to detect changes for a wide class of parametric settings. We provide theory that guides the choice of penalties to use for SUBSET, and that shows it has high power to detect changes regardless of whether only a few variates or many variates change. Empirical results show that SUBSET out-performs many existing approaches for detecting changes in mean in Gaussian data;additionally, unlike these alternative methods, it can be easily extended to non-Gaussian settings such as are appropriate for modelling counts of terrorist events.

关键词： binary segmentation likelihood ratio multivariate changepoint detection penalised cost function wild binary segmentation

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Efficient multiple change point detection for high-dimensional generalized linear models

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CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE 2023年第2期51卷 596-629页

作者： Wang, Xianru Liu, Bin Zhang, Xinsheng Liu, Yufeng Fudan Univ Sch Management Dept Stat & Data Sci Shanghai Peoples R China Univ N Carolina Carolina Ctr Genome Sci Linberger Comprehens Canc Ctr Dept Stat & Operat ResDept GenetDept Biostat Chapel Hill NC 27515 USA

Change point detection for high-dimensional data is an important yet challenging problem for many applications. In this article, we consider multiple change point detection in the context of high-dimensional generalized linear models, allowing the covariate dimension p to grow exponentially with the sample size n. The model considered is general and flexible in the sense that it covers various specific models as special cases. It can automatically account for the underlying data generation mechanism without specifying any prior knowledge about the number of change points. Based on dynamic programming and binary segmentation techniques, two algorithms are proposed to detect multiple change points, allowing the number of change points to grow with n. To further improve the computational efficiency, a more efficient algorithm designed for the case of a single change point is proposed. We present theoretical properties of our proposed algorithms, including estimation consistency for the number and locations of change points as well as consistency and asymptotic distributions for the underlying regression coefficients. Finally, extensive simulation studies and application to the Alzheimer's Disease Neuroimaging Initiative data further demonstrate the competitive performance of our proposed methods.

关键词： binary segmentation dynamic programming generalized linear models high dimensions

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