Modern Statistical Methods for Spatial and Multivariate Data

• Corporate Author: SpringerLink (Online service)
• Other Authors: Diawara, Norou (Editor, http://id.loc.gov/vocabulary/relators/edt)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2019.
• Edition: 1st ed. 2019.
• Series: STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health,
• Subjects: Applied mathematics.; Engineering mathematics.; Statistics .; Applications of Mathematics.; Statistical Theory and Methods.

 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. 

 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.