Download or read book Empirical Bayes methods for spatial data written by Hubert Kostal and published by . This book was released on 1985 with total page 442 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Empirical Bayes Methods Applied to Spatial Analysis Problems written by Grace M. Carter and published by . This book was released on 1973 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: A generalization of empirical Bayes estimators to the case in which the components have unequal variance is given. This estimator is applied to the spatial analysis problem of estimating the probability that a fire alarm reported from a particular street box signals a structural fire rather than a false alarm, rubbish fire or other emergency. The approach is to group alarm boxes into relatively homogeneous neighborhoods and to make empirical Bayes estimates of the 'probability structural' for each box in the neighborhood for yearly (1967-1969) Bronx data. The results are evaluated by measuring how a dispatching rule based on the estimates performs on 1970 data.

Download or read book Statistical Methods for Spatial Data Analysis written by Oliver Schabenberger and published by CRC Press. This book was released on 2017-01-27 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understanding spatial statistics requires tools from applied and mathematical statistics, linear model theory, regression, time series, and stochastic processes. It also requires a mindset that focuses on the unique characteristics of spatial data and the development of specialized analytical tools designed explicitly for spatial data analysis. Statistical Methods for Spatial Data Analysis answers the demand for a text that incorporates all of these factors by presenting a balanced exposition that explores both the theoretical foundations of the field of spatial statistics as well as practical methods for the analysis of spatial data. This book is a comprehensive and illustrative treatment of basic statistical theory and methods for spatial data analysis, employing a model-based and frequentist approach that emphasizes the spatial domain. It introduces essential tools and approaches including: measures of autocorrelation and their role in data analysis; the background and theoretical framework supporting random fields; the analysis of mapped spatial point patterns; estimation and modeling of the covariance function and semivariogram; a comprehensive treatment of spatial analysis in the spectral domain; and spatial prediction and kriging. The volume also delivers a thorough analysis of spatial regression, providing a detailed development of linear models with uncorrelated errors, linear models with spatially-correlated errors and generalized linear mixed models for spatial data. It succinctly discusses Bayesian hierarchical models and concludes with reviews on simulating random fields, non-stationary covariance, and spatio-temporal processes. Additional material on the CRC Press website supplements the content of this book. The site provides data sets used as examples in the text, software code that can be used to implement many of the principal methods described and illustrated, and updates to the text itself.

Download or read book Bayes and Empirical Bayes Methods for Data Analysis Second Edition written by Bradley P. Carlin and published by Chapman and Hall/CRC. This book was released on 2000-06-22 with total page 440 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent years, Bayes and empirical Bayes (EB) methods have continued to increase in popularity and impact. Building on the first edition of their popular text, Carlin and Louis introduce these methods, demonstrate their usefulness in challenging applied settings, and show how they can be implemented using modern Markov chain Monte Carlo (MCMC) methods. Their presentation is accessible to those new to Bayes and empirical Bayes methods, while providing in-depth coverage valuable to seasoned practitioners. With its broad appeal as a text for those in biomedical science, education, social science, agriculture, and engineering, this second edition offers a relatively gentle and comprehensive introduction for students and practitioners already familiar with more traditional frequentist statistical methods. Focusing on practical tools for data analysis, the book shows how properly structured Bayes and EB procedures typically have good frequentist and Bayesian performance, both in theory and in practice.

Download or read book Geospatial Health Data written by Paula Moraga and published by CRC Press. This book was released on 2019-11-26 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geospatial health data are essential to inform public health and policy. These data can be used to quantify disease burden, understand geographic and temporal patterns, identify risk factors, and measure inequalities. Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny describes spatial and spatio-temporal statistical methods and visualization techniques to analyze georeferenced health data in R. The book covers the following topics: Manipulate and transform point, areal, and raster data, Bayesian hierarchical models for disease mapping using areal and geostatistical data, Fit and interpret spatial and spatio-temporal models with the Integrated Nested Laplace Approximations (INLA) and the Stochastic Partial Differential Equation (SPDE) approaches, Create interactive and static visualizations such as disease maps and time plots, Reproducible R Markdown reports, interactive dashboards, and Shiny web applications that facilitate the communication of insights to collaborators and policy makers. The book features fully reproducible examples of several disease and environmental applications using real-world data such as malaria in The Gambia, cancer in Scotland and USA, and air pollution in Spain. Examples in the book focus on health applications, but the approaches covered are also applicable to other fields that use georeferenced data including epidemiology, ecology, demography or criminology. The book provides clear descriptions of the R code for data importing, manipulation, modeling and visualization, as well as the interpretation of the results. This ensures contents are fully reproducible and accessible for students, researchers and practitioners.

Download or read book Data Gathering Analysis and Protection of Privacy Through Randomized Response Techniques Qualitative and Quantitative Human Traits written by and published by Elsevier. This book was released on 2016-04-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Gathering, Analysis and Protection of Privacy through Randomized Response Techniques: Qualitative and Quantitative Human Traits tackles how to gather and analyze data relating to stigmatizing human traits. S.L. Warner invented RRT and published it in JASA, 1965. In the 50 years since, the subject has grown tremendously, with continued growth. This book comprehensively consolidates the literature to commemorate the inception of RR. Brings together all relevant aspects of randomized response and indirect questioning Tackles how to gather and analyze data relating to stigmatizing human traits Gives an encyclopedic coverage of the topic Covers recent developments and extrapolates to future trends

Download or read book Bayesian Methods for Data Analysis Third Edition written by Bradley P. Carlin and published by CRC Press. This book was released on 2008-06-30 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt: Broadening its scope to nonstatisticians, Bayesian Methods for Data Analysis, Third Edition provides an accessible introduction to the foundations and applications of Bayesian analysis. Along with a complete reorganization of the material, this edition concentrates more on hierarchical Bayesian modeling as implemented via Markov chain Monte Carlo (MCMC) methods and related data analytic techniques. New to the Third Edition New data examples, corresponding R and WinBUGS code, and homework problems Explicit descriptions and illustrations of hierarchical modeling—now commonplace in Bayesian data analysis A new chapter on Bayesian design that emphasizes Bayesian clinical trials A completely revised and expanded section on ranking and histogram estimation A new case study on infectious disease modeling and the 1918 flu epidemic A solutions manual for qualifying instructors that contains solutions, computer code, and associated output for every homework problem—available both electronically and in print Ideal for Anyone Performing Statistical Analyses Focusing on applications from biostatistics, epidemiology, and medicine, this text builds on the popularity of its predecessors by making it suitable for even more practitioners and students.

Download or read book Hierarchical Modeling and Analysis for Spatial Data written by Sudipto Banerjee and published by CRC Press. This book was released on 2003-12-17 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,

Download or read book Statistical Methods for Spatial Planning and Monitoring written by Silvestro Montrone and published by Springer Science & Business Media. This book was released on 2012-10-24 with total page 167 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book aims to investigate methods and techniques for spatial statistical analysis suitable to model spatial information in support of decision systems. Over the last few years there has been a considerable interest in these tools and in the role they can play in spatial planning and environmental modelling. One of the earliest and most famous definition of spatial planning was “a geographical expression to the economic, social, cultural and ecological policies of society”: borrowing from this point of view, this text shows how an interdisciplinary approach is an effective way to an harmonious integration of national policies with regional and local analysis. A wide range of spatial models and techniques is, also, covered: spatial data mining, point processes analysis, nearest neighbor statistics and cluster detection, Fuzzy Regression model and local indicators of spatial association; all of these tools provide the policy-maker with a valuable support to policy development.

Download or read book Hierarchical Modeling and Analysis for Spatial Data Second Edition written by Sudipto Banerjee and published by CRC Press. This book was released on 2014-09-12 with total page 587 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keep Up to Date with the Evolving Landscape of Space and Space-Time Data Analysis and Modeling Since the publication of the first edition, the statistical landscape has substantially changed for analyzing space and space-time data. More than twice the size of its predecessor, Hierarchical Modeling and Analysis for Spatial Data, Second Edition reflects the major growth in spatial statistics as both a research area and an area of application. New to the Second Edition New chapter on spatial point patterns developed primarily from a modeling perspective New chapter on big data that shows how the predictive process handles reasonably large datasets New chapter on spatial and spatiotemporal gradient modeling that incorporates recent developments in spatial boundary analysis and wombling New chapter on the theoretical aspects of geostatistical (point-referenced) modeling Greatly expanded chapters on methods for multivariate and spatiotemporal modeling New special topics sections on data fusion/assimilation and spatial analysis for data on extremes Double the number of exercises Many more color figures integrated throughout the text Updated computational aspects, including the latest version of WinBUGS, the new flexible spBayes software, and assorted R packages The Only Comprehensive Treatment of the Theory, Methods, and Software This second edition continues to provide a complete treatment of the theory, methods, and application of hierarchical modeling for spatial and spatiotemporal data. It tackles current challenges in handling this type of data, with increased emphasis on observational data, big data, and the upsurge of associated software tools. The authors also explore important application domains, including environmental science, forestry, public health, and real estate.

Download or read book Statistics for Spatial Data written by Noel Cressie and published by John Wiley & Sons. This book was released on 2015-03-18 with total page 931 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Wiley Classics Library consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. Spatial statistics — analyzing spatial data through statistical models — has proven exceptionally versatile, encompassing problems ranging from the microscopic to the astronomic. However, for the scientist and engineer faced only with scattered and uneven treatments of the subject in the scientific literature, learning how to make practical use of spatial statistics in day-to-day analytical work is very difficult. Designed exclusively for scientists eager to tap into the enormous potential of this analytical tool and upgrade their range of technical skills, Statistics for Spatial Data is a comprehensive, single-source guide to both the theory and applied aspects of spatial statistical methods. The hard-cover edition was hailed by Mathematical Reviews as an "excellent book which will become a basic reference." This paper-back edition of the 1993 edition, is designed to meet the many technological challenges facing the scientist and engineer. Concentrating on the three areas of geostatistical data, lattice data, and point patterns, the book sheds light on the link between data and model, revealing how design, inference, and diagnostics are an outgrowth of that link. It then explores new methods to reveal just how spatial statistical models can be used to solve important problems in a host of areas in science and engineering. Discussion includes: Exploratory spatial data analysis Spectral theory for stationary processes Spatial scale Simulation methods for spatial processes Spatial bootstrapping Statistical image analysis and remote sensing Computational aspects of model fitting Application of models to disease mapping Designed to accommodate the practical needs of the professional, it features a unified and common notation for its subject as well as many detailed examples woven into the text, numerous illustrations (including graphs that illuminate the theory discussed) and over 1,000 references. Fully balancing theory with applications, Statistics for Spatial Data, Revised Edition is an exceptionally clear guide on making optimal use of one of the ascendant analytical tools of the decade, one that has begun to capture the imagination of professionals in biology, earth science, civil, electrical, and agricultural engineering, geography, epidemiology, and ecology.

Download or read book Spatial Data Analysis written by Robert P. Haining and published by Cambridge University Press. This book was released on 2003-04-17 with total page 462 pages. Available in PDF, EPUB and Kindle. Book excerpt: Spatial Data Analysis: Theory and Practice, first published in 2003, provides a broad ranging treatment of the field of spatial data analysis. It begins with an overview of spatial data analysis and the importance of location (place, context and space) in scientific and policy related research. Covering fundamental problems concerning how attributes in geographical space are represented to the latest methods of exploratory spatial data analysis and spatial modeling, it is designed to take the reader through the key areas that underpin the analysis of spatial data, providing a platform from which to view and critically appreciate many of the key areas of the field. Parts of the text are accessible to undergraduate and master's level students, but it also contains sufficient challenging material that it will be of interest to geographers, social and economic scientists, environmental scientists and statisticians, whose research takes them into the area of spatial analysis.

Download or read book Estimation and Inference for Spatial and Spatio temporal Mixed Effects Models written by Casey M. Jelsema and published by . This book was released on 2013 with total page 169 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the most common goals of geostatistical analysis is that of spatial prediction, in other words: filling in the blank areas of the map. There are two popular methods for accomplishing spatial prediction. Either kriging, or Bayesian hierarchical models. Both methods require the inverse of the spatial covariance matrix of the data. As the sample size, n, becomes large, both of these methods become impractical. Reduced rank spatial models (RRSM) allow prediction on massive datasets without compromising the complexity of the spatial process. This dissertation focuses on RRSMs, particularly situations where the data follow non-Gaussian distributions. The manner in which data can be non-Gaussian varies, and we address multiple such situations. We begin by developing multivariate log-normal kriging and block kriging equations, and explain how to implement them for data that are compositional. We also propose a robust non-parametric approach to parameter estimation for kriging. Moving towards hierarchical models, we develop an empirical Bayes method to estimate parameters for heavy-tailed distributions. After, we turn to the problem of knot selection. The topic is under-represented in literature despite its importance to reduced rank spatial models. Finally, we explore fully Bayesian models for non-Gaussian data based on scale mixtures of Gaussians. The method is flexible enough to model a variety of distributional forms. Furthermore, we discuss how our models can be extended to consider dependencies in space as well as time.

Download or read book Hierarchical Modeling and Analysis for Spatial Data written by Sudipto Banerjee and published by CRC Press. This book was released on 2003-12-17 with total page 470 pages. Available in PDF, EPUB and Kindle. Book excerpt: Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis,

Download or read book Applied Spatial Data Analysis with R written by Roger S. Bivand and published by Springer Science & Business Media. This book was released on 2013-06-21 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applied Spatial Data Analysis with R, second edition, is divided into two basic parts, the first presenting R packages, functions, classes and methods for handling spatial data. This part is of interest to users who need to access and visualise spatial data. Data import and export for many file formats for spatial data are covered in detail, as is the interface between R and the open source GRASS GIS and the handling of spatio-temporal data. The second part showcases more specialised kinds of spatial data analysis, including spatial point pattern analysis, interpolation and geostatistics, areal data analysis and disease mapping. The coverage of methods of spatial data analysis ranges from standard techniques to new developments, and the examples used are largely taken from the spatial statistics literature. All the examples can be run using R contributed packages available from the CRAN website, with code and additional data sets from the book's own website. Compared to the first edition, the second edition covers the more systematic approach towards handling spatial data in R, as well as a number of important and widely used CRAN packages that have appeared since the first edition. This book will be of interest to researchers who intend to use R to handle, visualise, and analyse spatial data. It will also be of interest to spatial data analysts who do not use R, but who are interested in practical aspects of implementing software for spatial data analysis. It is a suitable companion book for introductory spatial statistics courses and for applied methods courses in a wide range of subjects using spatial data, including human and physical geography, geographical information science and geoinformatics, the environmental sciences, ecology, public health and disease control, economics, public administration and political science. The book has a website where complete code examples, data sets, and other support material may be found: http://www.asdar-book.org. The authors have taken part in writing and maintaining software for spatial data handling and analysis with R in concert since 2003.

Download or read book Handbook of Spatial Statistics written by Alan E. Gelfand and published by CRC Press. This book was released on 2010-03-19 with total page 622 pages. Available in PDF, EPUB and Kindle. Book excerpt: Assembling a collection of very prominent researchers in the field, the Handbook of Spatial Statistics presents a comprehensive treatment of both classical and state-of-the-art aspects of this maturing area. It takes a unified, integrated approach to the material, providing cross-references among chapters.The handbook begins with a historical intro

Download or read book The SAGE Handbook of Spatial Analysis written by A Stewart Fotheringham and published by SAGE. This book was released on 2008-12-22 with total page 529 pages. Available in PDF, EPUB and Kindle. Book excerpt: The widespread use of Geographical Information Systems (GIS) has significantly increased the demand for knowledge about spatial analytical techniques across a range of disciplines. As growing numbers of researchers realise they are dealing with spatial data, the demand for specialised statistical and mathematical methods designed to deal with spatial data is undergoing a rapid increase. Responding to this demand, The Handbook of Spatial Analysis is a comprehensive and authoritative discussion of issues and techniques in the field of Spatial Data Analysis. Its principal focus is on: • why the analysis of spatial data needs separate treatment • the main areas of spatial analysis • the key debates within spatial analysis • examples of the application of various spatial analytical techniques • problems in spatial analysis • areas for future research Aimed at an international audience of academics, The Handbook of Spatial Analysis will also prove essential to graduate level students and researchers in government agencies and the private sector.