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Titlebook: Non-Gaussian Autoregressive-Type Time Series; N. Balakrishna Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive li

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发表于 2025-3-21 17:03:44 | 显示全部楼层 |阅读模式
书目名称Non-Gaussian Autoregressive-Type Time Series
编辑N. Balakrishna
视频video
概述Brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data.Discusses the probabilistic and second-order properties of all models.Reviews the models available for
图书封面Titlebook: Non-Gaussian Autoregressive-Type Time Series;  N. Balakrishna Book 2021 The Editor(s) (if applicable) and The Author(s), under exclusive li
描述.This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential properties. This book classifies the stationary time-series models into different groups such as linear stationary models with non-Gaussian innovations, linear stationary models with non-Gaussian marginal distributions, product autoregressive models and minification models. Even though several non-Gaussian time-series models are available in the literature, most of them are focusing on the model structure and the probabilistic properties..
出版日期Book 2021
关键词non Gaussian time series; exponential autoregressive models; laplace autoregressive models; logistic au
版次1
doihttps://doi.org/10.1007/978-981-16-8162-2
isbn_softcover978-981-16-8164-6
isbn_ebook978-981-16-8162-2
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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r properties of all models.Reviews the models available for .This book brings together a variety of non-Gaussian autoregressive-type models to analyze time-series data. This book collects and collates most of the available models in the field and provide their probabilistic and inferential propertie
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ion-making processes (DMP) that conclude with the selection of optimal or satisfactory solutions require effective and efficient means of support. Spatial Decision Support Systems (SDSS) are computer systems developed to support DMP in which the problems have geographic dimensions and whose structur
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d handle spatial data. The scientific modelling component, represented by mathematical models of natural physical processes, usually is implemented in SDSS through specific software subsystems. Especially in the last seven years there has been great scientific interest in SDSS accompanied by a proli
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N. Balakrishnaqualitative data analysis was then performed. The findings demonstrate that in this case at least, espoused ‘shared understanding’ was limited. The paper also describes how further steps were successfully introduced which appear to improve the degree of mutual understanding.
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