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Titlebook: Advances in Time Series Methods and Applications; The A. Ian McLeod Fe Wai Keung Li,David A. Stanford,Hao Yu Book 2016 Springer Science+Bus

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发表于 2025-3-21 18:38:25 | 显示全部楼层 |阅读模式
期刊全称Advances in Time Series Methods and Applications
期刊简称The A. Ian McLeod Fe
影响因子2023Wai Keung Li,David A. Stanford,Hao Yu
视频video
发行地址Discusses a diverse range of state-of-the-art topics in time series analysis.Contains contributions from a number of researchers renowned for their time series work.Covers applications, methodologies,
学科分类Fields Institute Communications
图书封面Titlebook: Advances in Time Series Methods and Applications; The A. Ian McLeod Fe Wai Keung Li,David A. Stanford,Hao Yu Book 2016 Springer Science+Bus
影响因子This volume reviews and summarizes some of A. I. McLeod‘s significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.
Pindex Book 2016
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书目名称Advances in Time Series Methods and Applications影响因子(影响力)




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书目名称Advances in Time Series Methods and Applications网络公开度学科排名




书目名称Advances in Time Series Methods and Applications被引频次




书目名称Advances in Time Series Methods and Applications被引频次学科排名




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书目名称Advances in Time Series Methods and Applications年度引用学科排名




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书目名称Advances in Time Series Methods and Applications读者反馈学科排名




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Catching Uncertainty of Wind: A Blend of Sieve Bootstrap and Regime Switching Models for Probabilisegime switching models. Our new bootstrapped regime switching (BRS) model delivers highly competitive, sharp and calibrated ensembles of wind speed forecasts, governed by various states of wind direction, and imposes minimal requirements on the observed wind data. The proposed methodology is illustr
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,A Brief Derivation of the Asymptotic Distribution of Pearson’s Statistic and an Accurate Approximattribution is introduced as an accurate approximation to be utilized when the chi-squared distribution proves to be inadequate. It is also explained that the exact probability mass function of this test statistic can be readily determined from its moment-generating function via symbolic computations. Two illustrative numerical examples are included.
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The Doubly Adaptive LASSO for Vector Autoregressive Models,lation matrix function (Heyse, 1985, [.]) and Yule–Walker or ordinary least squares estimates of a vector time series. The existing papers ignore the partial lag autocorrelation information inherent in a VAR process. The procedure shows promising results for VAR models. The procedure excels in terms of VAR lag order identification.
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