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Modern statistical methods for spati...

Diawara, Norou.

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Modern statistical methods for spatial and multivariate data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modern statistical methods for spatial and multivariate data/ edited by Norou Diawara.
other author:	Diawara, Norou.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiv, 177 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Statistics. -
Online resource:	https://doi.org/10.1007/978-3-030-11431-2
ISBN:	9783030114312

Modern statistical methods for spatial and multivariate data
Modern statistical methods for spatial and multivariate data[electronic resource] /edited by Norou Diawara. - Cham :Springer International Publishing :2019. - xiv, 177 p. :ill., digital ;24 cm. - STEAM-H: science, technology, engineering, agriculture, mathematics & health,2520-193X. - STEAM-H..

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.

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.

ISBN: 9783030114312

Standard No.: 10.1007/978-3-030-11431-2doiSubjects--Topical Terms:

517247
Statistics.

LC Class. No.: QA276 / .M63 2019

Dewey Class. No.: 519

Modern statistical methods for spatial and multivariate data
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245 0 0 $a Modern statistical methods for spatial and multivariate data $h [electronic resource] / $c edited by Norou Diawara.
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505 0 $a 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.
520 $a 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.
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