Sequential Analysis-Design Methods and Applications最新文献_第9页

Detecting changes in the second moment structure of high-dimensional sensor-type data in a K-sample setting 在K样本设置中检测高维传感器类型数据的二阶矩结构的变化

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-01-15 DOI: 10.1080/07474946.2020.1823192

Nils Mause, A. Steland

引用次数: 2

Sequential algorithms for moving anomaly detection in networks 网络中运动异常检测的顺序算法

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-01-02 DOI: 10.1080/07474946.2020.1726678

Georgios Rovatsos, Shaofeng Zou, V. Veeravalli

{"title":"Sequential algorithms for moving anomaly detection in networks","authors":"Georgios Rovatsos, Shaofeng Zou, V. Veeravalli","doi":"10.1080/07474946.2020.1726678","DOIUrl":"https://doi.org/10.1080/07474946.2020.1726678","url":null,"abstract":"Abstract The problem of quickest moving anomaly detection in networks is studied. Initially, the observations are generated according to a prechange distribution. At some unknown but deterministic time, an anomaly emerges in the network. At each time instant, one node is affected by the anomaly and receives data from a post-change distribution. The anomaly moves across the network, and the node that it affects changes with time. However, the trajectory of the moving anomaly is assumed to be unknown. A discrete-time Markov chain is employed to model the unknown trajectory of the moving anomaly in the network. A windowed generalized likelihood ratio–based test is constructed and is shown to be asymptotically optimal. Other detection algorithms including the dynamic Shiryaev-Roberts test, a quickest change detection algorithm with recursive change point estimation, and a mixture cumulative sum (CUSUM) algorithm are also developed for this problem. Lower bounds on the mean time to false alarm are developed. Numerical results are further provided to compare their performances.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"39 1","pages":"31 - 6"},"PeriodicalIF":0.8,"publicationDate":"2020-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474946.2020.1726678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47495857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 16

CBM for testing multiple hypotheses with directional alternatives in sequential experiments CBM用于序列实验中具有方向选择的多个假设的检验

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-01-02 DOI: 10.1080/07474946.2020.1727166

K. Kachiashvili, J. K. Kachiashvili, I. Prangishvili

引用次数: 2

Editor’s Note 编者按

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-01-02 DOI: 10.1080/07474946.2020.1726688

Analúcia Danilevicz Pereira

引用次数: 0

Exponential inequalities for Mann’s stochastic algorithm Mann随机算法的指数不等式

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-01-02 DOI: 10.1080/07474946.2020.1726681

Chahira Allouti, Bahia Barache, A. Dahmani

引用次数: 0

Sequential controlled sensing for composite multihypothesis testing 序列控制传感复合多假设检验

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2019-10-24 DOI: 10.1080/07474946.2021.1912525

Aditya Deshmukh, S. Bhashyam, V. Veeravalli

引用次数: 11

High-confidence nonparametric fixed-width uncertainty intervals and applications to projected high-dimensional data and common mean estimation 高置信度非参数固定宽度不确定性区间及其在投影高维数据和共同均值估计中的应用

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2019-10-07 DOI: 10.1080/07474946.2021.1847966

A. Steland, Yuan-Tsung Chang

{"title":"High-confidence nonparametric fixed-width uncertainty intervals and applications to projected high-dimensional data and common mean estimation","authors":"A. Steland, Yuan-Tsung Chang","doi":"10.1080/07474946.2021.1847966","DOIUrl":"https://doi.org/10.1080/07474946.2021.1847966","url":null,"abstract":"Abstract Nonparametric two-stage procedures to construct fixed-width confidence intervals are studied to quantify uncertainty. It is shown that the validity of the random central limit theorem (RCLT) accompanied by a consistent and asymptotically unbiased estimator of the asymptotic variance already guarantees consistency and first-order as well as second-order efficiency of the two-stage procedures. This holds under the common asymptotics where the length of the confidence interval tends toward 0 as well as under the novel proposed high-confidence asymptotics where the confidence level tends toward 1. The approach is motivated by and applicable to data analysis from distributed big data with nonnegligible costs of data queries. The following problems are discussed: Fixed-width intervals for the mean, for a projection when observing high-dimensional data, and for the common mean when using nonlinear common mean estimators under order constraints. The procedures are investigated by simulations and illustrated by a real data analysis.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"40 1","pages":"97 - 124"},"PeriodicalIF":0.8,"publicationDate":"2019-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474946.2021.1847966","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47363101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Sequential minimum risk point estimation (MRPE) methodology for a normal mean under Linex loss plus sampling cost: First-order and second-order asymptotics Linex损失加采样代价下正态均值的序贯最小风险点估计方法:一阶和二阶渐近性

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2019-10-02 DOI: 10.1080/07474946.2019.1686937

N. Mukhopadhyay, Soumik Banerjee

引用次数: 3

Thanks to the Referees 感谢裁判

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2019-10-02 DOI: 10.1080/07474946.2019.1686938

引用次数: 0

A general theory of purely sequential minimum risk point estimation (MRPE) of a function of the mean in a normal distribution 正态分布中均值函数的纯序列最小风险点估计的一般理论

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2019-10-02 DOI: 10.1080/07474946.2019.1686885

N. Mukhopadhyay, Zhe Wang

{"title":"A general theory of purely sequential minimum risk point estimation (MRPE) of a function of the mean in a normal distribution","authors":"N. Mukhopadhyay, Zhe Wang","doi":"10.1080/07474946.2019.1686885","DOIUrl":"https://doi.org/10.1080/07474946.2019.1686885","url":null,"abstract":"Abstract A purely sequential minimum risk point estimation (MRPE) methodology with associated stopping time N is designed to come up with a useful estimation strategy. We work under an appropriately formulated weighted squared error loss (SEL) due to estimation of a function of μ, with plus linear cost of sampling from a population having both parameters unknown. A series of important first-order and second-order asymptotic (as c, the cost per unit sample, ) results is laid out including the first-order and second-order efficiency properties. Then, accurate sequential risk calculations are launched, which are then followed by two main results: (i) Theorem 4.1 shows an asymptotic risk efficiency property, and (ii) Theorem 5.1 shows an asymptotic second-order regret expansion associated with the proposed purely sequential MRPE strategy assuming suitable conditions on g(.). We also provide a bias-corrected version of the terminal estimator, We follow up with a number of interesting illustrations where Theorems 4.1–5.1 are readily exploited to conclude an asymptotic risk efficiency property and second-order regret expansion, respectively. A number of other interesting illustrations are highlighted where it is possible to verify the conclusions from Theorems 4.1–5.1 more directly with less stringent assumptions on the pilot sample size.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":"38 1","pages":"480 - 502"},"PeriodicalIF":0.8,"publicationDate":"2019-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/07474946.2019.1686885","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45421026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7