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

Critical review of bio‐inspired optimization techniques 生物启发优化技术综述

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-08-27 DOI: 10.1002/wics.1528

Anita Christaline Johnvictor, Vaishali Durgamahanthi, Ramya Meghana Pariti Venkata, Nishtha Jethi

{"title":"Critical review of bio‐inspired optimization techniques","authors":"Anita Christaline Johnvictor, Vaishali Durgamahanthi, Ramya Meghana Pariti Venkata, Nishtha Jethi","doi":"10.1002/wics.1528","DOIUrl":"https://doi.org/10.1002/wics.1528","url":null,"abstract":"In today's world of engineering evolution, the need for optimized design has led to development of a plethora of optimization algorithms. Right from hardware engineering design problems that need optimization of design parameters to software applications that require reduction of data sets, optimization algorithms play a vital role. These algorithms are either based on statistical measures or on heuristics. Traditional optimization algorithms use statistical methods in which the optimal solution may not be the global minimal point. These standard optimization techniques are more application specific and demand different parameter sets for different applications. Rather, the bio‐inspired meta‐heuristic algorithms act like black boxes enabling multiple applications with definite global optimal solutions. This review work gives an insight of various bio‐inspired optimization algorithms including dragonfly algorithm, the whale optimization algorithm, gray wolf optimizer, moth‐flame optimization algorithm, cuckoo optimization algorithm, artificial bee colony algorithm, ant colony optimization, grasshopper optimization algorithm, binary bat algorithm, salp algorithm, and the ant lion optimizer. The biological behaviors of the living things that lead to modeling of these algorithms have been discussed in detail. The parametric setting of each algorithm has been studied and their evaluation with benchmark test functions has been reviewed. Also their application to real‐world engineering design problems has been discussed. Based on these characteristics, the possibility to extend these algorithms to data set optimization, feature set reduction, or optimization has been discussed.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2020-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1528","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47957257","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

A review of h‐likelihood and hierarchical generalized linear model h‐似然和层次广义线性模型综述

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-08-25 DOI: 10.1002/wics.1527

Shaobo Jin, Youngjo Lee

{"title":"A review of h‐likelihood and hierarchical generalized linear model","authors":"Shaobo Jin, Youngjo Lee","doi":"10.1002/wics.1527","DOIUrl":"https://doi.org/10.1002/wics.1527","url":null,"abstract":"Fisher's classical likelihood has become the standard procedure to make inference for fixed unknown parameters. Recently, inferences of unobservable random variables, such as random effects, factors, missing values, etc., have become important in statistical analysis. Because Fisher's likelihood cannot have such unobservable random variables, the full Bayesian method is only available for inference. An alternative likelihood approach is proposed by Lee and Nelder. In the context of Fisher likelihood, the likelihood principle means that the likelihood function carries all relevant information regarding the fixed unknown parameters. Bjørnstad extended the likelihood principle to extended likelihood principle; all information in the observed data for fixed unknown parameters and unobservables are in the extended likelihood, such as the h‐likelihood. However, it turns out that the use of extended likelihood for inferences is not as straightforward as the Fisher likelihood. In this paper, we describe how to extract information of the data from the h‐likelihood. This provides a new way of statistical inferences in entire fields of statistical science.","PeriodicalId":47779,"journal":{"name":"Wiley Interdisciplinary Reviews-Computational Statistics","volume":null,"pages":null},"PeriodicalIF":3.2,"publicationDate":"2020-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/wics.1527","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45693409","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: 13

Issue Information 问题信息

IF 3.2 2区数学

Wiley Interdisciplinary Reviews-Computational Statistics Pub Date : 2020-08-07 DOI: 10.1002/wics.1475

引用次数: 0

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