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IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-04-16 DOI: 10.1002/wics.1473
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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":" ","pages":""},"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
{"title":"Review of current advances in survival analysis and frailty models","authors":"Usha Govindarajulu, R. D'Agostino","doi":"10.1002/wics.1504","DOIUrl":"https://doi.org/10.1002/wics.1504","url":null,"abstract":"In this article, we have presented a review of existing methods and trends in survival analysis and frailty models. The background has been presented for each topic discussed for survival and frailty models where the presentation flows from original methods to more advanced methods. This article has also shown various current methodologies that exist among survival and frailty models. The advantages and disadvantages of more recent methodologies are presented and discussed in this review.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"12 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1504","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41737607","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: 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
{"title":"Bayesian spatial and spatiotemporal models based on multiscale factorizations","authors":"Marco A. R. Ferreira","doi":"10.1002/wics.1509","DOIUrl":"https://doi.org/10.1002/wics.1509","url":null,"abstract":"We review the literature on spatial and spatiotemporal models based on spatial multiscale factorizations. Specifically, we review models based on wavelets and Kolaczyk–Huang factorizations for Gaussian and Poisson data. These multiscale models decompose spatial and spatiotemporal datasets into many small components, called multiscale coefficients, at multiple levels of spatial resolution. Then analysis proceeds independently for each multiscale coefficient. After that, aggregation equations are used to coherently combine the analyses from the multiple multiscale coefficients to obtain a statistical analysis at the original resolution level. The computational cost of such analysis grows linearly with sample size. Furthermore, computations for these models are scalable, parallelizable, and fast. Therefore, these multiscale models are tremendously useful for the analysis of massive spatial and spatiotemporal datasets.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1509","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47673378","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
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
{"title":"Animal movement models for multiple individuals","authors":"H. Scharf, F. Buderman","doi":"10.1002/wics.1506","DOIUrl":"https://doi.org/10.1002/wics.1506","url":null,"abstract":"Statistical models for animal movement provide tools that help ecologists and biologists learn how animals interact with their environment and each other. Efforts to develop increasingly realistic, implementable, and scientifically valuable methods for analyzing remotely observed trajectories have provided practitioners with a wide selection of models to help them understand animal behavior. Increasingly, researchers are interested in studying multiple animals jointly, which requires methods that can account for dependence across individuals. Dependence can arise for many reasons, including shared behavioral tendencies, familial relationships, and direct interactions on the landscape. We provide a synopsis of recent statistical methods for animal movement data applicable to settings in which inference is desired across multiple individuals. Highlights of these approaches include the ability to infer shared behavioral traits across a group of individuals and the ability to infer unobserved social networks summarizing dynamic relationships that manifest themselves in movement decisions.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1506","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45395796","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: 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
{"title":"A review of flow field forecasting: A high‐dimensional forecasting procedure","authors":"Kyle A. Caudle, Patrick S. Fleming, R. Hoover","doi":"10.1002/wics.1505","DOIUrl":"https://doi.org/10.1002/wics.1505","url":null,"abstract":"Forecasting, especially high‐dimensional forecasting, is becoming more and more sought after, particularly as computing resources increase in both size and speed. Flow field forecasting is a general purpose regression‐based forecasting method that has recently been expanded to high‐dimensional settings. In this article, we provide an overview of the flow field forecasting methodology, with a particular emphasis on environments where the number of candidate predictor variables is large, potentially larger than the number of observations.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1505","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42758852","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: 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
{"title":"Issue Information","authors":"","doi":"10.1002/wics.1472","DOIUrl":"https://doi.org/10.1002/wics.1472","url":null,"abstract":"","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1472","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44065857","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: 0
Stationary count time series models 固定计数时间序列模型
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-02-13 DOI: 10.1002/wics.1502
C. Weiß
{"title":"Stationary count time series models","authors":"C. Weiß","doi":"10.1002/wics.1502","DOIUrl":"https://doi.org/10.1002/wics.1502","url":null,"abstract":"During the last 20–30 years, there was a remarkable growth in interest on approaches for stationary count time series. We consider popular classes of models for such time series, including thinning‐based models, conditional regression models, and Hidden‐Markov models. We review and compare important members of these model families, having regard to stochastic properties such as the dispersion and autocorrelation structure. Our survey covers univariate and multivariate count data, as well as unbounded and bounded counts. We also discuss an illustrative data example. Besides this critical presentation of the current state‐of‐the‐art, some existing challenges and opportunities for future research are identified.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44954649","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: 20
Randomization 随机化
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-02-07 DOI: 10.1002/wics.91
B. Manly
{"title":"Randomization","authors":"B. Manly","doi":"10.1002/wics.91","DOIUrl":"https://doi.org/10.1002/wics.91","url":null,"abstract":"There are three aspects of randomization in statistics that are considered here. The first aspect is randomization as part of a sampling design to estimate one or more parameters for a statistical population such as all the farms in a certain area of a country, based on information obtained about the parameters from only a part of the population. The second aspect is using randomization as part of an experimental design to ensure that the allocation of treatment levels to the experimental units is not biased in any way. For example, the test of a new drug for relieving the symptoms of a disease might involve this drug being randomly allocated to half of a group of patients, while the other half of the patients receive a standard drug that is used for the disease. Finally, the third aspect is using randomization to test some statistical hypothesis. For example, to see if there is a significant difference between two drugs for the treatment of a disease in terms of some suitable outcome measure, the observed mean difference between means for this outcome measure might be compared to the distribution of mean differences that is obtained by randomly reallocating the observed values of the measure to the drugs. The null hypothesis being tested would then be that each of the observed values of the measure was equally likely to have occurred with each of the two drugs. Copyright © 2010 John Wiley & Sons, Inc.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":"30 1","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91159646","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: 79
Model exploration using conditional visualization 使用条件可视化的模型探索
IF 3.2 2区 数学
Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-02-07 DOI: 10.1002/wics.1503
C. Hurley
{"title":"Model exploration using conditional visualization","authors":"C. Hurley","doi":"10.1002/wics.1503","DOIUrl":"https://doi.org/10.1002/wics.1503","url":null,"abstract":"Ideally, statistical parametric model fitting is followed by various summary tables which show predictor contributions, visualizations which assess model assumptions and goodness of fit, and test statistics which compare models. In contrast, modern machine‐learning fits are usually black box in nature, offer high‐performing predictions but suffer from an interpretability deficit. We examine how the paradigm of conditional visualization can be used to address this, specifically to explain predictor contributions, assess goodness of fit, and compare multiple, competing fits. We compare visualizations from techniques including trellis, condvis, visreg, lime, partial dependence, and ice plots. Our examples use random forest fits, but all techniques presented are model agnostic.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":" ","pages":""},"PeriodicalIF":3.2,"publicationDate":"2020-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1503","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41820022","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: 1
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