检索结果-内蒙古大学图书馆

Relating and comparing methods for detecting changes in mean

STAT 2020年第1期9卷 1-11页

作者： Fearnhead, Paul Rigaill, Guillem Univ Lancaster Dept Math & Stat Lancaster LA1 4YF England Univ Evry Univ Paris Saclay CNRS INRAEInst Plant Sci Paris Saclay IPS2 F-91405 Orsay France Univ Paris CNRS Inst Plant Sci Paris Saclay IPS2 INRAE F-91405 Orsay France Univ Evry Univ Paris Saclay CNRS Lab Math & Modelisat Evry F-91037 Evry France

In recent years, there have been a large number of proposed approaches to detecting changes in mean. A natural question for an analyst is which method is most appropriate for their applications. Answering this question is difficult because current empirical studies often give conflicting conclusions. This paper aims to show the similarities and differences between different changepoint methods. We highlight that there are two aspects to estimating changepoints: estimating the number of changes and estimating their locations, and that comparisons should separately evaluate these two aspects. We perform an extensive comparison of different methods across a range of simulation scenarios and provide code and full results for an interested practitioner to extend this comparison to more methods or different scenarios.

关键词： binary segmentation breakpoints dynamic programming model error penalized likelihood structural breaks

来源：评论

学校读者我要写书评

暂无评论

Statistical inference for multiple change-point models

引用

SCANDINAVIAN JOURNAL OF STATISTICS 2020年第4期47卷 1149-1170页

作者： Wang, Wu He, Xuming Zhu, Zhongyi King Abdullah Univ Sci & Technol Stat Program Thuwal Saudi Arabia Univ Michigan Dept Stat Ann Arbor MI 48109 USA Fudan Univ Dept Stat 670 Guoshun Rd Shanghai Peoples R China

In this article, we propose a new technique for constructing confidence intervals for the mean of a noisy sequence with multiple change-points. We use the weighted bootstrap to generalize the bootstrap aggregating or bagging estimator. A standard deviation formula for the bagging estimator is introduced, based on which smoothed confidence intervals are constructed. To further improve the performance of the smoothed interval for weak signals, we suggest a strategy of adaptively choosing between the percentile intervals and the smoothed intervals. A new intensity plot is proposed to visualize the pattern of the change-points. We also propose a new change-point estimator based on the intensity plot, which has superior performance in comparison with the state-of-the-art segmentation methods. The finite sample performance of the confidence intervals and the change-point estimator are evaluated through Monte Carlo studies and illustrated with a real data example.

关键词： bagging estimator binary segmentation bootstrap copy number variation multiple change-points

来源：评论

学校读者我要写书评

暂无评论

A Distributed Intrusion Detection Scheme for Cloud Computing

引用

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

来源：评论

学校读者我要写书评

暂无评论

High-dimensional change point detection for mean and location parameters

High-dimensional change point detection for mean and locatio...

引用

作者： Yu, Mengjia University of Illinois – Urbana-Champaign

学位级别：博士

Change point inference refers to detection of structural breaks of a sequence observation, which may have one or more distributional shifts subject to models such as mean or covariance changes. In this dissertation, we consider the offline multiple change point problem that the sample size is fixed in advance or after observation. In particular, we concentrate on high-dimensional setup where the dimension $p$ can be much larger than the sample size $n$ and traditional distribution assumptions can easily fail. The goal is to employ non-parametric approaches to identify change points without involving intermediate estimation to cross-sectional dependence. In the first part, we consider cumulative sum (CUSUM) statistics that are widely used in the change point inference and identification. We study two problems for high-dimensional mean vectors based on the $\ell^{\infty}$-norm of the CUSUM statistics. For the problem of testing for the 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 $p \gg n$. Once a change point is detected, we estimate the change point location by maximizing the $\ell^{\infty}$-norm of the generalized CUSUM statistics at two different weighting scales. The first estimator is based on the covariance stationary CUSUM statistics, and we prove its consistency in estimating the location at the nearly parametric rate $n^{-1/2}$ for sub-exponential observations. The second estimator is based on non-stationary CUSUM stat

关键词： High-dimensional data Change point analysis CUSUM Gaussian approximation Bootstrap binary segmentation U-statistics

来源：评论

学校读者我要写书评

暂无评论

A deep-learning toolkit for visualization and interpretation of segmented medical images

引用

CELL REPORTS METHODS 2021年第7期1卷 100107页

作者： Ghosal, Sambuddha Shah, Pratik MIT Program Media Arts & Sci & Media Lab 20 Ames St Cambridge MA 02139 USA

Generalizability of deep-learning (DL) model performance is not well understood and uses anecdotal assumptions for increasing training data to improve segmentation of medical images. We report statistical methods for visual interpretation of DL models trained using ImageNet initialization with natural-world (T-II) and supervised learning with medical images (L-MI) for binary segmentation of skin cancer, prostate tumors, and kidneys. An algorithm for computation of Dice scores from union and intersections of individual output masks was developed for synergistic segmentation by T-II and L-MI models. Stress testing with non-Gaussian distributions of infrequent clinical labels and images showed that sparsity of natural-world and domain medical images can counterintuitively reduce type I and type II errors of DL models. A toolkit of 30 T-II and L-MI models, code, and visual outputs of 59,967 images is shared to identify the target and non-target medical image pixels and clinical labels to explain the performance of DL models.

关键词： generalized deep learning binary segmentation statistics image processing model explanation medical images

来源：评论

学校读者我要写书评

暂无评论

An Adaptive Intrusion Detection Scheme for Cloud Computing

引用

INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2019年第4期10卷 53-70页

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

To provide dynamic resource management, live virtual machine migration is used to move a virtual machine from one host to another. However, virtual machine migration poses challenges to cloud intrusion detection systems because movement of VMs from one host to another makes it difficult to create a consistent normal profile for anomaly detection. Hence, there is a need to provide an adaptive anomaly detection system capable of adapting to changes that occur in the cloud data during VM migration. To achieve this, the authors proposed a scheme for adaptive IDS for Cloud computing. The proposed adaptive scheme is comprised of four components: an ant colony optimization-based feature selection component, a statistical time series change point detection component, adaptive classification, and model update component, and a detection component. The proposed adaptive scheme was evaluated using simulated datasets collected from vSphere and performance comparison shows improved performance over existing techniques.

关键词： Anomaly Detection Ant Colony Optimization binary segmentation Cloud Security DDoS Machine Learning Stochastic Gradient Descent Time Series

来源：评论

学校读者我要写书评

暂无评论

Subgroup identification via homogeneity pursuit for dense longitudinal/spatial data

引用

STATISTICS IN MEDICINE 2019年第17期38卷 3256-3271页

作者： Li, Jialiang Yue, Mu Zhang, Wenyang Natl Univ Singapore Dept Stat & Appl Probabil Singapore Singapore Duke NUS Med Sch Singapore Singapore Singapore Eye Res Inst Singapore Singapore Univ Elect Sci & Technol China Sch Math Sci Chengdu 611731 Sichuan Peoples R China Univ York Dept Math York N Yorkshire England

In the clinical trial community, it is usually not easy to find a treatment that benefits all patients since the reaction to treatment may differ substantially across different patient subgroups. The heterogeneity of treatment effect plays an essential role in personalized medicine. To facilitate the development of tailored therapies and improve the treatment efficacy, it is important to identify subgroups that exhibit different treatment effects. We consider a very general framework for subgroup identification via the homogeneity pursuit methods usually employed in econometric time series analysis. The change point detection algorithm in our procedure is most suitable for analyzing dense longitudinal or spatial data which are quite common for biomedical studies these days. We demonstrate that our proposed method is fast and accurate through extensive numerical studies. In particular, our method is illustrated by analyzing a diffusion tensor imaging data set.

关键词： binary segmentation change point detection dense longitudinal data homogeneity pursuit personalized medicine treatment recommendation

来源：评论

学校读者我要写书评

暂无评论

Changepoint Detection in the Presence of Outliers

引用

JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 2019年第525期114卷 169-183页

作者： Fearnhead, Paul Rigaill, Guillem Univ Lancaster Dept Math & Stat Lancaster LA1 4YF England Univ Paris Diderot Sorbonne Paris Cite Univ EvryCNRSUMR 9213UMR1403 Univ Paris SudInst Plant Sci Paris SaclayINRA Paris France Univ Evry Val dEssonne Lab Math Modelisat Evry LaMME CNRS UMR 8071ENSIIEUSCINRA Paris France

Many traditional methods for identifying changepoints can struggle in the presence of outliers, or when the noise is heavy-tailed. Often they will infer additional changepoints to fit the outliers. To overcome this problem, data often needs to be preprocessed to remove outliers, though this is difficult for applications where the data needs to be analyzed online. We present an approach to changepoint detection that is robust to the presence of outliers. The idea is to adapt existing penalized cost approaches for detecting changes so that they use loss functions that are less sensitive to outliers. We argue that loss functions that are bounded, such as the classical biweight loss, are particularly suitableas we show that only bounded loss functions are robust to arbitrarily extreme outliers. We present an efficient dynamic programming algorithm that can find the optimal segmentation under our penalized cost criteria. Importantly, this algorithm can be used in settings where the data needs to be analyzed online. We show that we can consistently estimate the number of changepoints, and accurately estimate their locations, using the biweight loss function. We demonstrate the usefulness of our approach for applications such as analyzing well-log data, detecting copy number variation, and detecting tampering of wireless devices. Supplementary materials for this article are available online.

关键词： binary segmentation Biweight loss Cusum M-estimation Penalized likelihood Robust statistics

来源：评论

学校读者我要写书评

暂无评论

A MOSUM procedure for the estimation of multiple random change points

引用

BERNOULLI 2018年第1期24卷 526-564页

作者： Eichinger, Birte Kirch, Claudia Karlsruhe Inst Technol KIT Inst Stochast Kaiserstr 89 D-76133 Karlsruhe Germany Otto von Guericke Univ Inst Math Stochast Postfach 4120 D-39106 Magdeburg Germany

In this work, we investigate statistical properties of change point estimators based on moving sum statistics. We extend results for testing in a classical situation with multiple deterministic change points by allowing for random exogenous change points that arise in Hidden Markov or regime switching models among others. To this end, we consider a multiple mean change model with possible time series errors and prove that the number and location of change points are estimated consistently by this procedure. Additionally, we derive rates of convergence for the estimation of the location of the change points and show that these rates are strict by deriving the limit distribution of properly scaled estimators. Because the small sample behavior depends crucially on how the asymptotic (long-run) variance of the error sequence is estimated, we propose to use moving sum type estimators for the (long-run) variance and derive their asymptotic properties. While they do not estimate the variance consistently at every point in time, they can still be used to consistently estimate the number and location of the changes. In fact, this inconsistency can even lead to more precise estimators for the change points. Finally, some simulations illustrate the behavior of the estimators in small samples showing that its performance is very good compared to existing methods.

关键词： binary segmentation change point hidden Markov model moving sum statistics regime switching model

来源：评论

学校读者我要写书评

暂无评论

Investigation of bone age assessment with convolutional neural network by using DoG filtering and a tarous wavelet as preprocessing techniques 6

Investigation of bone age assessment with convolutional neur...

引用

6th International Conference on Control Engineering and Information Technology (CEIT)

作者： Asad, Mahdi Nezhad Canturk, Ismail Genc, Fatih Z. Ozyilmaz, Lale Yildiz Tech Univ Dept Elect & Commun Engn Istanbul Turkey

ISBN: (纸本)9781538676417

Bone age assessment is one of the most important problems to assess Pediatric growth. Various methods have been investigated to develop bone age assessment. The study of bone age assessment helps to diagnose disorders in growth progress. It is very common to monitor growth progress with left hand x-ray images. There are many method such as Tanner-Whitehouse ITWI, Greulich and Pyle (G&P) methods. In this paper, we applied convolutional neural network resnet50 by preprocessing the images using different filters based on edge detection to compare effects on CNN accuracy. The main purpose of the paper is to compare the effect of DoG filtering and a trous wavelet as preprocessing techniques on bone-age assessment. It has been proved that suggested methods improved the accuracy of CNN (resnet50) in filtered images compared to the result of the non-filtered ones. The ages between 0 and 7 are analyzed to assess bone for female, male, and female male together. It is observed that there is over% 14 improvement for Female for Male %11 and for male and female %10 percent between filtered images and non-filtered images. The proposed method reached% 87.78 female and% 80 for male within 7.9 month.

关键词： bone age assessment- x-ray radiology left hand convolutional neural network a wavelet filter Differential Gaussian filter binary segmentation resnet50

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：