Wiley Interdisciplinary Reviews-Computational Statistics最新文献_第10页

30 Years of space–time covariance functions 30 年时空协方差函数

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-05-20 DOI: 10.1002/wics.1512

E. Porcu, R. Furrer, D. Nychka

{"title":"30 Years of space–time covariance functions","authors":"E. Porcu, R. Furrer, D. Nychka","doi":"10.1002/wics.1512","DOIUrl":"https://doi.org/10.1002/wics.1512","url":null,"abstract":"In this article, we provide a comprehensive review of space–time covariance functions. As for the spatial domain, we focus on either the d‐dimensional Euclidean space or on the unit d‐dimensional sphere. We start by providing background information about (spatial) covariance functions and their properties along with different types of covariance functions. While we focus primarily on Gaussian processes, many of the results are independent of the underlying distribution, as the covariance only depends on second‐moment relationships. We discuss properties of space–time covariance functions along with the relevant results associated with spectral representations. Special attention is given to the Gneiting class of covariance functions, which has been especially popular in space–time geostatistical modeling. We then discuss some techniques that are useful for constructing new classes of space–time covariance functions. Separate treatment is reserved for spectral models, as well as to what are termed models with special features. We also discuss the problem of estimation of parametric classes of space–time covariance functions. An outlook concludes the paper.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2020-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1512","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45698321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 62

Analysis of shape data: From landmarks to elastic curves. 形状数据分析：从地标到弹性曲线

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-05-01 Epub Date: 2020-01-17 DOI: 10.1002/wics.1495

Karthik Bharath, Sebastian Kurtek

{"title":"Analysis of shape data: From landmarks to elastic curves.","authors":"Karthik Bharath, Sebastian Kurtek","doi":"10.1002/wics.1495","DOIUrl":"10.1002/wics.1495","url":null,"abstract":"<p><p>Proliferation of high-resolution imaging data in recent years has led to sub-stantial improvements in the two popular approaches for analyzing shapes of data objects based on landmarks and/or continuous curves. We provide an expository account of elastic shape analysis of parametric planar curves representing shapes of two-dimensional (2D) objects by discussing its differences, and its commonalities, to the landmark-based approach. Particular attention is accorded to the role of reparameterization of a curve, which in addition to rotation, scaling and translation, represents an important shape-preserving transformation of a curve. The transition to the curve-based approach moves the mathematical setting of shape analysis from finite-dimensional non-Euclidean spaces to infinite-dimensional ones. We discuss some of the challenges associated with the infinite-dimensionality of the shape space, and illustrate the use of geometry-based methods in the computation of intrinsic statistical summaries and in the definition of statistical models on a 2D imaging dataset consisting of mouse vertebrae. We conclude with an overview of the current state-of-the-art in the field.</p>","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8357314/pdf/nihms-1704274.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39316954","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial machine learning for cybersecurity and computer vision: Current developments and challenges 网络安全和计算机视觉的对抗性机器学习:当前的发展和挑战

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-04-21 DOI: 10.1002/wics.1511

B. Xi

{"title":"Adversarial machine learning for cybersecurity and computer vision: Current developments and challenges","authors":"B. Xi","doi":"10.1002/wics.1511","DOIUrl":"https://doi.org/10.1002/wics.1511","url":null,"abstract":"We provide a comprehensive overview of adversarial machine learning focusing on two application domains, that is, cybersecurity and computer vision. Research in adversarial machine learning addresses a significant threat to the wide application of machine learning techniques—they are vulnerable to carefully crafted attacks from malicious adversaries. For example, deep neural networks fail to correctly classify adversarial images, which are generated by adding imperceptible perturbations to clean images. We first discuss three main categories of attacks against machine learning techniques—poisoning attacks, evasion attacks, and privacy attacks. Then the corresponding defense approaches are introduced along with the weakness and limitations of the existing defense approaches. We notice adversarial samples in cybersecurity and computer vision are fundamentally different. While adversarial samples in cybersecurity often have different properties/distributions compared with training data, adversarial images in computer vision are created with minor input perturbations. This further complicates the development of robust learning techniques, because a robust learning technique must withstand different types of attacks.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2020-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43224376","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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: 15

Issue Information 问题信息

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-04-16 DOI: 10.1002/wics.1473

引用次数: 0

Nonparametric covariance estimation with shrinkage toward stationary models 向平稳模型收缩的非参数协方差估计

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-03-20 DOI: 10.1002/wics.1507

T. A. Blake, Yoonkyung Lee

{"title":"Nonparametric covariance estimation with shrinkage toward stationary models","authors":"T. A. Blake, Yoonkyung Lee","doi":"10.1002/wics.1507","DOIUrl":"https://doi.org/10.1002/wics.1507","url":null,"abstract":"Estimation of an unstructured covariance matrix is difficult because of the challenges posed by parameter space dimensionality and the positive‐definiteness constraint that estimates should satisfy. We consider a general nonparametric covariance estimation framework for longitudinal data using the Cholesky decomposition of a positive‐definite matrix. The covariance matrix of time‐ordered measurements is diagonalized by a lower triangular matrix with unconstrained entries that are statistically interpretable as parameters for a varying coefficient autoregressive model. Using this dual interpretation of the Cholesky decomposition and allowing for irregular sampling time points, we treat covariance estimation as bivariate smoothing and cast it in a regularization framework for desired forms of simplicity in covariance models. Viewing stationarity as a form of simplicity or parsimony in covariance, we model the varying coefficient function with components depending on time lag and its orthogonal direction separately and penalize the components that capture the nonstationarity in the fitted function. We demonstrate construction of a covariance estimator using the smoothing spline framework. Simulation studies establish the advantage of our approach over alternative estimators proposed in the longitudinal data setting. We analyze a longitudinal dataset to illustrate application of the methodology and compare our estimates to those resulting from alternative models.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2020-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1507","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47761789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Review of current advances in survival analysis and frailty models 回顾当前生存分析和衰弱模型的进展

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-03-17 DOI: 10.1002/wics.1504

Usha Govindarajulu, R. D'Agostino

引用次数: 6

Bayesian spatial and spatiotemporal models based on multiscale factorizations 基于多尺度因子分解的贝叶斯时空模型

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-03-17 DOI: 10.1002/wics.1509

Marco A. R. Ferreira

引用次数: 2

Animal movement models for multiple individuals 多个体动物运动模型

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-03-09 DOI: 10.1002/wics.1506

H. Scharf, F. Buderman

引用次数: 9

A review of flow field forecasting: A high‐dimensional forecasting procedure 流场预测综述:一种高维预测方法

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-02-21 DOI: 10.1002/wics.1505

Kyle A. Caudle, Patrick S. Fleming, R. Hoover

引用次数: 1

Issue Information 问题信息

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-02-16 DOI: 10.1002/wics.1472

引用次数: 0