| 书目名称 | Modern Statistical Methods for Spatial and Multivariate Data | | 编辑 | Norou Diawara | | 视频video | | | 概述 | Exposes new research directions and solutions in spatio-temporal and multivariate data.Increases understanding of models in medical and engineering areas.Features recent advances in spatio-temporal as | | 丛书名称 | STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health | | 图书封面 |  | | 描述 | This contributed volume features invited papers on current models and statistical methods for spatial and multivariate data. With a focus on recent advances in statistics, topics include spatio-temporal aspects, classification techniques, the multivariate outcomes with zero and doubly-inflated data, discrete choice modelling, copula distributions, and feasible algorithmic solutions. Special emphasis is placed on applications such as the use of spatial and spatio-temporal models for rainfall in South Carolina and the multivariate sparse areal mixed model for the Census dataset for the state of Iowa. Articles use simulated and aggregated data examples to show the flexibility and wide applications of proposed techniques..Carefully peer-reviewed and pedagogically presented for a broad readership, this volume is suitable for graduate and postdoctoral students interested in interdisciplinary research. Researchers in applied statistics and sciences will find thisbook an important resource on the latest developments in the field. In keeping with the STEAM-H series, the editors hope to inspire interdisciplinary understanding and collaboration.. . . | | 出版日期 | Book 2019 | | 关键词 | optimization; simulation; modeling; multivariate data analysis; spatio-temporal techniques; functional re | | 版次 | 1 | | doi | https://doi.org/10.1007/978-3-030-11431-2 | | isbn_ebook | 978-3-030-11431-2Series ISSN 2520-193X Series E-ISSN 2520-1948 | | issn_series | 2520-193X | | copyright | Springer Nature Switzerland AG 2019 |
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发表于 2025-3-21 17:37:29
