Modern statistical methods for spatial and multivariate data
著者
書誌事項
Modern statistical methods for spatial and multivariate data
(STEAM-H: science, technology, engineering, agriculture, mathematics & health)
Springer, c2019
大学図書館所蔵 全3件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
  スウェーデン
  ノルウェー
  アメリカ
注記
Includes bibliographical references
内容説明・目次
内容説明
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 this book 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.
目次
A. Working, M. Alqawba, and N. Diawara: Functional Form of Markovian Attribute-level Best-Worst Discrete Choice Modelling.- D. Hitchcock, H. Liu, and S. Zahra Samadi.- Spatial and Spatio-temporal Analysis of Precipitation Data from South Carolina.- D. Musgrove, D. Young, J. Hughes, and L. E. Eberly: A sparse areal mixed model for multivariate outcomes, with an application to zero-inflated Census data.- E. M. Maboudou-Tchao: Wavelet Kernels for Support Matrix Machines.- S. A. Janse and K. L. Thompson: Properties of the number of iterations of a feasible solutions algorithm.- R. Dey and M. S. Mulekar: A Primer of Statistical Methods for Classification.- M. Sheth-Chandra, N. R. Chaganty, and R. T. Sabo: A Doubly-Inflated Poisson Distribution and Regression Model.- J. Mathews, S. Sen, and I. Das: Multivariate Doubly-Inflated Negative Binomial Distribution using Gaussian Copula.- J. Lorio, N. Diawara, and L. Waller: Quantifying spatio-temporal characteristics via Moran's statistics.
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