Modern Statistical Methods for Spatial and Multivariate Data

1. Front Matter

   Pages i-xiv

   PDF

2. Functional Form of Markovian Attribute-Level Best-Worst Discrete Choice Modeling

   • Amanda Working, Mohammed Alqawba, Norou Diawara

   Pages 1-30

3. Spatial and Spatio-Temporal Analysis of Precipitation Data from South Carolina

   • Haigang Liu, David B. Hitchcock, S. Zahra Samadi

   Pages 31-50

4. A Sparse Areal Mixed Model for Multivariate Outcomes, with an Application to Zero-Inflated Census Data

   • Donald Musgrove, Derek S. Young, John Hughes, Lynn E. Eberly

   Pages 51-74

5. Wavelet Kernels for Support Matrix Machines

   • Edgard M. Maboudou-Tchao

   Pages 75-93

6. Properties of the Number of Iterations of a Feasible Solutions Algorithm

   • Sarah A. Janse, Katherine L. Thompson

   Pages 95-105

7. A Primer of Statistical Methods for Classification

   • Rajarshi Dey, Madhuri S. Mulekar

   Pages 107-129

8. A Doubly-Inflated Poisson Distribution and Regression Model

   • Manasi Sheth-Chandra, N. Rao Chaganty, Roy T. Sabo

   Pages 131-145

9. Multivariate Doubly-Inflated Negative Binomial Distribution Using Gaussian Copula

   • Joseph Mathews, Sumen Sen, Ishapathik Das

   Pages 147-161

10. Quantifying Spatio-Temporal Characteristics via Moran’s Statistics

    • Jennifer L. Matthews, Norou Diawara, Lance A. Waller

    Pages 163-177

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.




         
      

• optimization
• simulation
• modeling
• multivariate data analysis
• spatio-temporal techniques
• functional representations
• spatial analysis
• spatio-temporal analysis
• statistical methods
• poisson distribution

• Department of Mathematics and Statistics, Old Dominion University, Norfolk, USA

  Norou Diawara

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