Download or read book Dimension Reduced Modeling of Spatio temporal Processes with Applications to Statistical Downscaling written by Jenný Brynjarsdóttir and published by . This book was released on 2011 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract: The field of spatial and spatio-temporal statistics is increasingly faced with the challenge of very large datasets. Examples include data obtained from remote sensing satellites, global weather stations, outputs from climate models and medical imagery. The classical approach to spatial and spatio-temporal modeling is extremely computationally expensive when the datasets are large. Dimension-reduced modeling approach has proved to be effective in such situations. In this thesis I focus on the problem of modeling two spatio-temporal processes where the primary goal is to predict one process from the other and where the datasets for both processes are large.

Download or read book Bayesian Modeling of Spatio Temporal Data with R written by Sujit Sahu and published by CRC Press. This book was released on 2022-02-23 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied sciences, both physical and social, such as atmospheric, biological, climate, demographic, economic, ecological, environmental, oceanic and political, routinely gather large volumes of spatial and spatio-temporal data in order to make wide ranging inference and prediction. Ideally such inferential tasks should be approached through modelling, which aids in estimation of uncertainties in all conclusions drawn from such data. Unified Bayesian modelling, implemented through user friendly software packages, provides a crucial key to unlocking the full power of these methods for solving challenging practical problems. Key features of the book: • Accessible detailed discussion of a majority of all aspects of Bayesian methods and computations with worked examples, numerical illustrations and exercises • A spatial statistics jargon buster chapter that enables the reader to build up a vocabulary without getting clouded in modeling and technicalities • Computation and modeling illustrations are provided with the help of the dedicated R package bmstdr, allowing the reader to use well-known packages and platforms, such as rstan, INLA, spBayes, spTimer, spTDyn, CARBayes, CARBayesST, etc • Included are R code notes detailing the algorithms used to produce all the tables and figures, with data and code available via an online supplement • Two dedicated chapters discuss practical examples of spatio-temporal modeling of point referenced and areal unit data • Throughout, the emphasis has been on validating models by splitting data into test and training sets following on the philosophy of machine learning and data science This book is designed to make spatio-temporal modeling and analysis accessible and understandable to a wide audience of students and researchers, from mathematicians and statisticians to practitioners in the applied sciences. It presents most of the modeling with the help of R commands written in a purposefully developed R package to facilitate spatio-temporal modeling. It does not compromise on rigour, as it presents the underlying theories of Bayesian inference and computation in standalone chapters, which would be appeal those interested in the theoretical details. By avoiding hard core mathematics and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.

Download or read book Predictive Modeling of Spatio temporal Datasets in High Dimensions written by Linchao Chen and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatio-temporal datasets in many research areas may be very high-dimensional and have complicated dependence structures in both space and time. Various dimension reduction techniques have been introduced to extract key information from single or multiple datasets. Standard dimension-reduced Bayesian hierarchical modeling approaches use a few spatial basis functions to capture the spatial structure and model temporal dynamics through their coefficients. However, these approaches are not always satisfactory in predictive modeling of complicated systems. It is challenging to build statistical models that are computationally efficient for very large datasets and able to adequately model the dependence structure. In this dissertation, we focus on developing modeling strategies for high-dimensional spatio-temporal datasets in the context of predictive modeling, especially when standard dimension reduction methods do not perform well. We discuss modeling strategies to synthesize multiple high-dimensional information sources and produce accurate predictions. We consider the sea surface temperature (SST) prediction problem of Berliner et al. [2000] and demonstrate challenges and advantages of incorporating high-dimensional climate model output along with temperature observations to improve predictive accuracy. Furthermore, we develop a two-scale modeling strategy for dimension-reduced modeling. This modeling strategy is able to capture both large-scale and local dynamics using a large-scale component derived from standard dimension reduction techniques and a local component customized for each pre-partitioned subregion. Empirical experiments show that this strategy is more efficient in capturing variability when standard dimension reduction methods do not perform well. It delivers more balanced results in the presence of outliers. We also discuss its potential as a spatially varying modeling approach. Finally, we gather examples of how one can use coarse scale information in dimension reduction, EOF analysis, predictive modeling, generation of partitions, and provide discussion on potential strategies to choose appropriate aggregation scales.

Download or read book Spatio temporal Statistical Models with Application to Atmospheric Processes written by Christopher Kim Wikle and published by . This book was released on 1996 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Empirical statistical Downscaling written by Rasmus E. Benestad and published by World Scientific. This book was released on 2008 with total page 228 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical-statistical downscaling (ESD) is a method for estimating how local climatic variables are affected by large-scale climatic conditions. ESD has been applied to local climate/weather studies for years, but there are few ? if any ? textbooks on the subject. It is also anticipated that ESD will become more important and commonplace in the future, as anthropogenic global warming proceeds. Thus, a textbook on ESD will be important for next-generation climate scientists.

Download or read book Spatio Temporal Modeling of Nonlinear Distributed Parameter Systems written by and published by Springer. This book was released on 2011-08-14 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Modern Statistical Methods for Spatial and Multivariate Data written by Norou Diawara and published by Springer. This book was released on 2019-06-29 with total page 177 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Modelling Longitudinal and Spatially Correlated Data written by Timothy G. Gregoire and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt: Correlated data arise in numerous contexts across a wide spectrum of subject-matter disciplines. Modeling such data present special challenges and opportunities that have received increasing scrutiny by the statistical community in recent years. In October 1996 a group of 210 statisticians and other scientists assembled on the small island of Nantucket, U. S. A. , to present and discuss new developments relating to Modelling Longitudinal and Spatially Correlated Data: Methods, Applications, and Future Direc tions. Its purpose was to provide a cross-disciplinary forum to explore the commonalities and meaningful differences in the source and treatment of such data. This volume is a compilation of some of the important invited and volunteered presentations made during that conference. The three days and evenings of oral and displayed presentations were arranged into six broad thematic areas. The session themes, the invited speakers and the topics they addressed were as follows: • Generalized Linear Models: Peter McCullagh-"Residual Likelihood in Linear and Generalized Linear Models" • Longitudinal Data Analysis: Nan Laird-"Using the General Linear Mixed Model to Analyze Unbalanced Repeated Measures and Longi tudinal Data" • Spatio---Temporal Processes: David R. Brillinger-"Statistical Analy sis of the Tracks of Moving Particles" • Spatial Data Analysis: Noel A. Cressie-"Statistical Models for Lat tice Data" • Modelling Messy Data: Raymond J. Carroll-"Some Results on Gen eralized Linear Mixed Models with Measurement Error in Covariates" • Future Directions: Peter J.

Download or read book Spatio Temporal Data Analysis at Scale Using Models Based on Gaussian Processes written by and published by . This book was released on 2017 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gaussian processes are the most commonly used statistical model for spatial and spatio-temporal processes that vary continuously. They are broadly applicable in the physical sciences and engineering and are also frequently used to approximate the output of complex computer models, deterministic or stochastic. We undertook research related to theory, computation, and applications of Gaussian processes as well as some work on estimating extremes of distributions for which a Gaussian process assumption might be inappropriate. Our theoretical contributions include the development of new classes of spatial-temporal covariance functions with desirable properties and new results showing that certain covariance models lead to predictions with undesirable properties. To understand how Gaussian process models behave when applied to deterministic computer models, we derived what we believe to be the first significant results on the large sample properties of estimators of parameters of Gaussian processes when the actual process is a simple deterministic function. Finally, we investigated some theoretical issues related to maxima of observations with varying upper bounds and found that, depending on the circumstances, standard large sample results for maxima may or may not hold. Our computational innovations include methods for analyzing large spatial datasets when observations fall on a partially observed grid and methods for estimating parameters of a Gaussian process model from observations taken by a polar-orbiting satellite. In our application of Gaussian process models to deterministic computer experiments, we carried out some matrix computations that would have been infeasible using even extended precision arithmetic by focusing on special cases in which all elements of the matrices under study are rational and using exact arithmetic. The applications we studied include total column ozone as measured from a polar-orbiting satellite, sea surface temperatures over the Pacific Ocean, and annual temperature extremes at a site in New York City. In each of these applications, our theoretical and computational innovations were directly motivated by the challenges posed by analyzing these and similar types of data.

Download or read book Statistical Downscaling and Bias Correction for Climate Research written by Douglas Maraun and published by Cambridge University Press. This book was released on 2018-01-18 with total page 365 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and practical guide, providing technical background and user context for researchers, graduate students, practitioners and decision makers. This book presents the main approaches and describes their underlying assumptions, skill and limitations. Guidelines for the application of downscaling and the use of downscaled information in practice complete the volume.

Download or read book Efficient Parameterization and Estimation of Spatio temporal Dynamic Models written by (Bill) Ke Xu and published by . This book was released on 2004 with total page 152 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation consists of two separate but related topics in spatio-temporal modeling. In the first part we investigate the problem of GEM (General EM) estimation of spatio-temporal models. We propose a spatio-temporal dynamic model formulation with parameter matrices further specified based on science and/or common spatial models. Several parame-terization strategies are proposed and analytical or computational closed form GEM update equations are derived for each. We apply the methodology to a diffusion space-time model in a simulation study and also apply a dimension-reduced model to a U.S. Palmer Drought Severity Index (PDSI) data set. In the second part we look at a convolution kernel approach to modeling diffusive propagation in the framework of Bayesian hierarchical spatio-temporal dynamic models. The diffusive propagation is modeled through the displacement of the convolution kernel. Computation efficiency is achieved by using the FFT and spectral dimension reduction. The method is applied to a thunderstorm nowcasting problem in Sydney, Australia.

Download or read book Theoretical Aspects of Spatial Temporal Modeling written by Gareth William Peters and published by Springer. This book was released on 2015-11-05 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: ​This book provides a modern introductory tutorial on specialized theoretical aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter provides up-to-date coverage of particle association measures that underpin the theoretical properties of recently developed random set methods in space and time otherwise known as the class of probability hypothesis density framework (PHD filters). The second chapter gives an overview of recent advances in Monte Carlo methods for Bayesian filtering in high-dimensional spaces. In particular, the chapter explains how one may extend classical sequential Monte Carlo methods for filtering and static inference problems to high dimensions and big-data applications. The third chapter presents an overview of generalized families of processes that extend the class of Gaussian process models to heavy-tailed families known as alpha-stable processes. In particular, it covers aspects of characterization via the spectral measure of heavy-tailed distributions and then provides an overview of their applications in wireless communications channel modeling. The final chapter concludes with an overview of analysis for probabilistic spatial percolation methods that are relevant in the modeling of graphical networks and connectivity applications in sensor networks, which also incorporate stochastic geometry features.

Download or read book Statistical and Geometric Modeling of Spatio temporal Patterns for Video Understanding written by Pavan Turaga and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Spatio temporal Models written by Alexandre Loureiros Rodrigues and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Models and Methods for Computationally Efficient Analysis of Large Spatial and Spatio temporal Data written by Chengwei Yuan and published by . This book was released on 2011 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Reducing Spatio temporal Data written by Liam Steadman and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Handbook of Mathematical Geosciences written by B.S. Daya Sagar and published by Springer. This book was released on 2018-06-25 with total page 914 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences.