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.
