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

Bayesian sequential joint detection and estimation under multiple hypotheses 多假设下贝叶斯序列联合检测与估计

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-03-27 DOI: 10.1080/07474946.2022.2043053

Dominik Reinhard, Michael Fauss, A. Zoubir

{"title":"Bayesian sequential joint detection and estimation under multiple hypotheses","authors":"Dominik Reinhard, Michael Fauss, A. Zoubir","doi":"10.1080/07474946.2022.2043053","DOIUrl":"https://doi.org/10.1080/07474946.2022.2043053","url":null,"abstract":"Abstract We consider the problem of jointly testing multiple hypotheses and estimating a random parameter of the underlying distribution. This problem is investigated in a sequential setup under mild assumptions on the underlying random process. The optimal method minimizes the expected number of samples while ensuring that the average detection/estimation errors do not exceed a certain level. After converting the constrained problem to an unconstrained one, we characterize the general solution by a nonlinear Bellman equation, which is parameterized by a set of cost coefficients. A strong connection between the derivatives of the cost function with respect to the coefficients and the detection/estimation errors of the sequential procedure is derived. Based on this fundamental property, we further show that for suitably chosen cost coefficients the solutions of the constrained and the unconstrained problem coincide. We present two approaches to finding the optimal coefficients. For the first approach, the final optimization problem is converted into a linear program, whereas the second approach solves it with a projected gradient ascent. To illustrate the theoretical results, we consider two problems for which the optimal schemes are designed numerically. Using Monte Carlo simulations, it is validated that the numerical results agree with the theory.","PeriodicalId":48879,"journal":{"name":"Sequential Analysis-Design Methods and Applications","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42945349","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: 3

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":null,"pages":null},"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 Tests of Multiple Hypotheses Controlling False Discovery and Nondiscovery Rates. 控制错误发现率和未发现率的多假设序列检验。

IF 0.8 4区数学

Sequential Analysis-Design Methods and Applications Pub Date : 2020-01-01 Epub Date: 2020-05-13 DOI: 10.1080/07474946.2020.1726686

Jay Bartroff, Jinlin Song

引用次数: 15

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":null,"pages":null},"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