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

FERAL

书目名称Non-Gaussian Autoregressive-Type Time Series影响因子(影响力)




书目名称Non-Gaussian Autoregressive-Type Time Series影响因子(影响力)学科排名




书目名称Non-Gaussian Autoregressive-Type Time Series网络公开度




书目名称Non-Gaussian Autoregressive-Type Time Series网络公开度学科排名




书目名称Non-Gaussian Autoregressive-Type Time Series被引频次




书目名称Non-Gaussian Autoregressive-Type Time Series被引频次学科排名




书目名称Non-Gaussian Autoregressive-Type Time Series年度引用




书目名称Non-Gaussian Autoregressive-Type Time Series年度引用学科排名




书目名称Non-Gaussian Autoregressive-Type Time Series读者反馈




书目名称Non-Gaussian Autoregressive-Type Time Series读者反馈学科排名

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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

Handedness

Spangle

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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HARD

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

Nefarious

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.

结构

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