Wiley Interdisciplinary Reviews-Computational Statistics最新文献_第9页

A review study of functional autoregressive models with application to energy forecasting 功能自回归模型在能源预测中的应用综述

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-07-28 DOI: 10.1002/wics.1525

Ying Chen, T. Koch, K. Lim, Xiaofei Xu, Nazgul Zakiyeva

{"title":"A review study of functional autoregressive models with application to energy forecasting","authors":"Ying Chen, T. Koch, K. Lim, Xiaofei Xu, Nazgul Zakiyeva","doi":"10.1002/wics.1525","DOIUrl":"https://doi.org/10.1002/wics.1525","url":null,"abstract":"In this data‐rich era, it is essential to develop advanced techniques to analyze and understand large amounts of data and extract the underlying information in a flexible way. We provide a review study on the state‐of‐the‐art statistical time series models for univariate and multivariate functional data with serial dependence. In particular, we review functional autoregressive (FAR) models and their variations under different scenarios. The models include the classic FAR model under stationarity; the FARX and pFAR model dealing with multiple exogenous functional variables and large‐scale mixed‐type exogenous variables; the vector FAR model and common functional principal component technique to handle multiple dimensional functional time series; and the warping FAR, varying coefficient‐FAR and adaptive FAR models to handle seasonal variations, slow varying effects and the more challenging cases of structural changes or breaks respectively. We present the models’ setup and detail the estimation procedure. We discuss the models’ applicability and illustrate the numerical performance using real‐world data of high‐resolution natural gas flows in the high‐pressure gas pipeline network of Germany. We conduct 1‐day and 14‐days‐ahead out‐of‐sample forecasts of the daily gas flow curves. We observe that the functional time series models generally produce stable out‐of‐sample forecast accuracy.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1525","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44328711","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: 7

Bayesian and frequentist testing for differences between two groups with parametric and nonparametric two‐sample tests 用参数和非参数两样本检验对两组之间差异的贝叶斯和频繁度检验

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-07-13 DOI: 10.1002/wics.1523

Riko Kelter

引用次数: 15

Robust linear regression for high‐dimensional data: An overview 高维数据的稳健线性回归：综述

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-07-08 DOI: 10.1002/wics.1524

P. Filzmoser, K. Nordhausen

{"title":"Robust linear regression for high‐dimensional data: An overview","authors":"P. Filzmoser, K. Nordhausen","doi":"10.1002/wics.1524","DOIUrl":"https://doi.org/10.1002/wics.1524","url":null,"abstract":"Digitization as the process of converting information into numbers leads to bigger and more complex data sets, bigger also with respect to the number of measured variables. This makes it harder or impossible for the practitioner to identify outliers or observations that are inconsistent with an underlying model. Classical least‐squares based procedures can be affected by those outliers. In the regression context, this means that the parameter estimates are biased, with consequences on the validity of the statistical inference, on regression diagnostics, and on the prediction accuracy. Robust regression methods aim at assigning appropriate weights to observations that deviate from the model. While robust regression techniques are widely known in the low‐dimensional case, researchers and practitioners might still not be very familiar with developments in this direction for high‐dimensional data. Recently, different strategies have been proposed for robust regression in the high‐dimensional case, typically based on dimension reduction, on shrinkage, including sparsity, and on combinations of such techniques. A very recent concept is downweighting single cells of the data matrix rather than complete observations, with the goal to make better use of the model‐consistent information, and thus to achieve higher efficiency of the parameter estimates.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1524","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46937358","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: 24

Advances in statistical modeling of spatial extremes 空间极值统计模型研究进展

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-07-01 DOI: 10.1002/wics.1537

Raphael Huser, J. Wadsworth

{"title":"Advances in statistical modeling of spatial extremes","authors":"Raphael Huser, J. Wadsworth","doi":"10.1002/wics.1537","DOIUrl":"https://doi.org/10.1002/wics.1537","url":null,"abstract":"The classical modeling of spatial extremes relies on asymptotic models (i.e., max‐stable or r‐Pareto processes) for block maxima or peaks over high thresholds, respectively. However, at finite levels, empirical evidence often suggests that such asymptotic models are too rigidly constrained, and that they do not adequately capture the frequent situation where more severe events tend to be spatially more localized. In other words, these asymptotic models have a strong tail dependence that persists at increasingly high levels, while data usually suggest that it should weaken instead. Another well‐known limitation of classical spatial extremes models is that they are either computationally prohibitive to fit in high dimensions, or they need to be fitted using less efficient techniques. In this review paper, we describe recent progress in the modeling and inference for spatial extremes, focusing on new models that have more flexible tail structures that can bridge asymptotic dependence classes, and that are more easily amenable to likelihood‐based inference for large datasets. In particular, we discuss various types of random scale constructions, as well as the conditional spatial extremes model, which have recently been getting increasing attention within the statistics of extremes community. We illustrate some of these new spatial models on two different environmental applications.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1537","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45974489","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: 64

A review of Bayesian group selection approaches for linear regression models 线性回归模型的贝叶斯群选择方法综述

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-06-29 DOI: 10.1002/wics.1513

Wei Lai, Ray‐Bing Chen

引用次数: 3

Issue Information 问题信息

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-06-07 DOI: 10.1002/wics.1474

引用次数: 0

Advance of the sufficient dimension reduction 充分降维的推进

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-06-03 DOI: 10.1002/wics.1516

Weiqiang Hang, Yingcun Xia

引用次数: 0

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":" ","pages":""},"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":"12 3","pages":""},"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":" ","pages":""},"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