Modelling Longitudinal And Spatially Correlated Data

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Modelling Longitudinal and Spatially Correlated Data

Timothy G. Gregoire,David R. Brillinger,Peter Diggle,Estelle Russek-Cohen,William G. Warren,Russell D. Wolfinger
Modelling Longitudinal and Spatially Correlated Data Book PDF

Author : Timothy G. Gregoire,David R. Brillinger,Peter Diggle,Estelle Russek-Cohen,William G. Warren,Russell D. Wolfinger
Publisher : Springer Science & Business Media
Page : 404 pages
File Size : 53,5 Mb
Release : 2012-12-06
Category : Mathematics
ISBN : 9781461206996

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Modelling Longitudinal and Spatially Correlated Data by Timothy G. Gregoire,David R. Brillinger,Peter Diggle,Estelle Russek-Cohen,William G. Warren,Russell D. Wolfinger Pdf

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.

Longitudinal Data Analysis

Ikuko Funatogawa,Takashi Funatogawa
Longitudinal Data Analysis Book PDF

Author : Ikuko Funatogawa,Takashi Funatogawa
Publisher : Springer
Page : 141 pages
File Size : 42,8 Mb
Release : 2019-02-04
Category : Mathematics
ISBN : 9789811000775

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Longitudinal Data Analysis by Ikuko Funatogawa,Takashi Funatogawa Pdf

This book provides a new analytical approach for dynamic data repeatedly measured from multiple subjects over time. Random effects account for differences across subjects. Auto-regression in response itself is often used in time series analysis. In longitudinal data analysis, a static mixed effects model is changed into a dynamic one by the introduction of the auto-regression term. Response levels in this model gradually move toward an asymptote or equilibrium which depends on covariates and random effects. The book provides relationships of the autoregressive linear mixed effects models with linear mixed effects models, marginal models, transition models, nonlinear mixed effects models, growth curves, differential equations, and state space representation. State space representation with a modified Kalman filter provides log likelihoods for maximum likelihood estimation, and this representation is suitable for unequally spaced longitudinal data. The extension to multivariate longitudinal data analysis is also provided. Topics in medical fields, such as response-dependent dose modifications, response-dependent dropouts, and randomized controlled trials are discussed. The text is written in plain terms understandable for researchers in other disciplines such as econometrics, sociology, and ecology for the progress of interdisciplinary research.

Contemporary Statistical Models for the Plant and Soil Sciences

Oliver Schabenberger,Francis J. Pierce
Contemporary Statistical Models for the Plant and Soil Sciences Book PDF

Author : Oliver Schabenberger,Francis J. Pierce
Publisher : CRC Press
Page : 762 pages
File Size : 48,6 Mb
Release : 2001-11-13
Category : Mathematics
ISBN : 9781420040197

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Contemporary Statistical Models for the Plant and Soil Sciences by Oliver Schabenberger,Francis J. Pierce Pdf

Despite its many origins in agronomic problems, statistics today is often unrecognizable in this context. Numerous recent methodological approaches and advances originated in other subject-matter areas and agronomists frequently find it difficult to see their immediate relation to questions that their disciplines raise. On the other hand, statisticians often fail to recognize the riches of challenging data analytical problems contemporary plant and soil science provides. The first book to integrate modern statistics with crop, plant and soil science, Contemporary Statistical Models for the Plant and Soil Sciences bridges this gap. The breadth and depth of topics covered is unusual. Each of the main chapters could be a textbook in its own right on a particular class of data structures or models. The cogent presentation in one text allows research workers to apply modern statistical methods that otherwise are scattered across several specialized texts. The combination of theory and application orientation conveys ìwhyî a particular method works and ìhowî it is put in to practice. About the downloadable resources The accompanying downloadable resources are a key component of the book. For each of the main chapters additional sections of text are available that cover mathematical derivations, special topics, and supplementary applications. It supplies the data sets and SAS code for all applications and examples in the text, macros that the author developed, and SAS tutorials ranging from basic data manipulation to advanced programming techniques and publication quality graphics. Contemporary statistical models can not be appreciated to their full potential without a good understanding of theory. They also can not be applied to their full potential without the aid of statistical software. Contemporary Statistical Models for the Plant and Soil Science provides the essential mix of theory and applications of statistical methods pertinent to research in life sciences.

Linear Mixed Models for Longitudinal Data

Geert Verbeke,Geert Molenberghs
Linear Mixed Models for Longitudinal Data Book PDF

Author : Geert Verbeke,Geert Molenberghs
Publisher : Springer Science & Business Media
Page : 570 pages
File Size : 48,5 Mb
Release : 2009-05-12
Category : Mathematics
ISBN : 9781441903006

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Linear Mixed Models for Longitudinal Data by Geert Verbeke,Geert Molenberghs Pdf

This book provides a comprehensive treatment of linear mixed models for continuous longitudinal data. Next to model formulation, this edition puts major emphasis on exploratory data analysis for all aspects of the model, such as the marginal model, subject-specific profiles, and residual covariance structure. Further, model diagnostics and missing data receive extensive treatment. Sensitivity analysis for incomplete data is given a prominent place. Most analyses were done with the MIXED procedure of the SAS software package, but the data analyses are presented in a software-independent fashion.

Antedependence Models for Longitudinal Data

Dale L. Zimmerman,Vicente A. Núñez-Antón
Antedependence Models for Longitudinal Data Book PDF

Author : Dale L. Zimmerman,Vicente A. Núñez-Antón
Publisher : CRC Press
Page : 288 pages
File Size : 46,6 Mb
Release : 2009-08-19
Category : Mathematics
ISBN : 1420064274

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Antedependence Models for Longitudinal Data by Dale L. Zimmerman,Vicente A. Núñez-Antón Pdf

The First Book Dedicated to This Class of Longitudinal Models Although antedependence models are particularly useful for modeling longitudinal data that exhibit serial correlation, few books adequately cover these models. By gathering results scattered throughout the literature, Antedependence Models for Longitudinal Data offers a convenient, systematic way to learn about antedependence models. Illustrated with numerous examples, the book also covers some important statistical inference procedures associated with these models. After describing unstructured and structured antedependence models and their properties, the authors discuss informal model identification via simple summary statistics and graphical methods. They then present formal likelihood-based procedures for normal antedependence models, including maximum likelihood and residual maximum likelihood estimation of parameters as well as likelihood ratio tests and penalized likelihood model selection criteria for the model’s covariance structure and mean structure. The authors also compare the performance of antedependence models to other models commonly used for longitudinal data. With this book, readers no longer have to search across widely scattered journal articles on the subject. The book provides a thorough treatment of the properties and statistical inference procedures of various antedependence models.

Generalized Linear and Nonlinear Models for Correlated Data

Edward F. Vonesh
Generalized Linear and Nonlinear Models for Correlated Data Book PDF

Author : Edward F. Vonesh
Publisher : SAS Institute
Page : 529 pages
File Size : 46,5 Mb
Release : 2014-07-07
Category : Mathematics
ISBN : 9781629592305

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Generalized Linear and Nonlinear Models for Correlated Data by Edward F. Vonesh Pdf

Edward Vonesh's Generalized Linear and Nonlinear Models for Correlated Data: Theory and Applications Using SAS is devoted to the analysis of correlated response data using SAS, with special emphasis on applications that require the use of generalized linear models or generalized nonlinear models. Written in a clear, easy-to-understand manner, it provides applied statisticians with the necessary theory, tools, and understanding to conduct complex analyses of continuous and/or discrete correlated data in a longitudinal or clustered data setting. Using numerous and complex examples, the book emphasizes real-world applications where the underlying model requires a nonlinear rather than linear formulation and compares and contrasts the various estimation techniques for both marginal and mixed-effects models. The SAS procedures MIXED, GENMOD, GLIMMIX, and NLMIXED as well as user-specified macros will be used extensively in these applications. In addition, the book provides detailed software code with most examples so that readers can begin applying the various techniques immediately. This book is part of the SAS Press program.

Analysis of Longitudinal Data

Peter Diggle
Analysis of Longitudinal Data Book PDF

Author : Peter Diggle
Publisher : Oxford University Press
Page : 408 pages
File Size : 46,5 Mb
Release : 2002-06-20
Category : Language Arts & Disciplines
ISBN : 0198524846

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Analysis of Longitudinal Data by Peter Diggle Pdf

This important text has been completely revised and expanded to become the most up-to-date and thorough professional reference text in this fast-moving area of biostatistics (medical statistics). This new edition contains an additional two chapters. The first of these discusses fully parametric models for discrete repeated measures data. The second explores statistical models for time-dependent predictors where there may be feedback between the predictor and response variables.

Topics in Modelling of Clustered Data

Marc Aerts,Geert Molenberghs,Louise M. Ryan,Helena Geys
Topics in Modelling of Clustered Data Book PDF

Author : Marc Aerts,Geert Molenberghs,Louise M. Ryan,Helena Geys
Publisher : CRC Press
Page : 336 pages
File Size : 46,9 Mb
Release : 2002-05-29
Category : Mathematics
ISBN : 9781420035889

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Topics in Modelling of Clustered Data by Marc Aerts,Geert Molenberghs,Louise M. Ryan,Helena Geys Pdf

Many methods for analyzing clustered data exist, all with advantages and limitations in particular applications. Compiled from the contributions of leading specialists in the field, Topics in Modelling of Clustered Data describes the tools and techniques for modelling the clustered data often encountered in medical, biological, environmental, and social science studies. It focuses on providing a comprehensive treatment of marginal, conditional, and random effects models using, among others, likelihood, pseudo-likelihood, and generalized estimating equations methods. The authors motivate and illustrate all aspects of these models in a variety of real applications. They discuss several variations and extensions, including individual-level covariates and combined continuous and discrete outcomes. Flexible modelling with fractional and local polynomials, omnibus lack-of-fit tests, robustification against misspecification, exact, and bootstrap inferential procedures all receive extensive treatment. The applications discussed center primarily, but not exclusively, on developmental toxicity, which leads naturally to discussion of other methodologies, including risk assessment and dose-response modelling. Clearly written, Topics in Modelling of Clustered Data offers a practical, easily accessible survey of important modelling issues. Overview models give structure to a multitude of approaches, figures help readers visualize model characteristics, and a generous use of examples illustrates all aspects of the modelling process.

Hierarchical Modeling and Analysis for Spatial Data

Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand
Hierarchical Modeling and Analysis for Spatial Data Book PDF

Author : Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand
Publisher : CRC Press
Page : 470 pages
File Size : 41,6 Mb
Release : 2003-12-17
Category : Mathematics
ISBN : 9781135438081

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Hierarchical Modeling and Analysis for Spatial Data by Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand Pdf

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,

Hierarchical Modeling and Analysis for Spatial Data, Second Edition

Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand
Hierarchical Modeling and Analysis for Spatial Data Second Edition Book PDF

Author : Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand
Publisher : CRC Press
Page : 587 pages
File Size : 41,9 Mb
Release : 2014-09-12
Category : Mathematics
ISBN : 9781439819173

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Hierarchical Modeling and Analysis for Spatial Data, Second Edition by Sudipto Banerjee,Bradley P. Carlin,Alan E. Gelfand Pdf

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.

Spatial Cluster Modelling

Andrew B. Lawson,David G.T. Denison
Spatial Cluster Modelling Book PDF

Author : Andrew B. Lawson,David G.T. Denison
Publisher : CRC Press
Page : 304 pages
File Size : 46,6 Mb
Release : 2002-05-16
Category : Mathematics
ISBN : 9781420035414

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Spatial Cluster Modelling by Andrew B. Lawson,David G.T. Denison Pdf

Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal cluster modelling. Many figures, some in full color, complement the text, and a single section of references cited makes it easy to locate source material. Leading specialists in the field of cluster modelling authored each chapter, and an introduction by the editors to each chapter provides a cohesion not typically found in contributed works. Spatial Cluster Modelling thus offers a singular opportunity to explore this exciting new field, understand its techniques, and apply them in your own research.

Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data

Brajendra C. Sutradhar
Advances and Challenges in Parametric and Semi parametric Analysis for Correlated Data Book PDF

Author : Brajendra C. Sutradhar
Publisher : Springer
Page : 256 pages
File Size : 40,7 Mb
Release : 2016-06-15
Category : Mathematics
ISBN : 9783319312606

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Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data by Brajendra C. Sutradhar Pdf

This proceedings volume contains eight selected papers that were presented in the International Symposium in Statistics (ISS) 2015 On Advances in Parametric and Semi-parametric Analysis of Multivariate, Time Series, Spatial-temporal, and Familial-longitudinal Data, held in St. John’s, Canada from July 6 to 8, 2015. The main objective of the ISS-2015 was the discussion on advances and challenges in parametric and semi-parametric analysis for correlated data in both continuous and discrete setups. Thus, as a reflection of the theme of the symposium, the eight papers of this proceedings volume are presented in four parts. Part I is comprised of papers examining Elliptical t Distribution Theory. In Part II, the papers cover spatial and temporal data analysis. Part III is focused on longitudinal multinomial models in parametric and semi-parametric setups. Finally Part IV concludes with a paper on the inferences for longitudinal data subject to a challenge of important covariates selection from a set of large number of covariates available for the individuals in the study.

Handbook of Spatial Statistics

Alan E. Gelfand,Peter Diggle,Peter Guttorp,Montserrat Fuentes
Handbook of Spatial Statistics Book PDF

Author : Alan E. Gelfand,Peter Diggle,Peter Guttorp,Montserrat Fuentes
Publisher : CRC Press
Page : 622 pages
File Size : 49,6 Mb
Release : 2010-03-19
Category : Mathematics
ISBN : 9781420072884

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Handbook of Spatial Statistics by Alan E. Gelfand,Peter Diggle,Peter Guttorp,Montserrat Fuentes Pdf

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

Spatial Analysis

John T. Kent,Kanti V. Mardia
Spatial Analysis Book PDF

Author : John T. Kent,Kanti V. Mardia
Publisher : John Wiley & Sons
Page : 404 pages
File Size : 45,8 Mb
Release : 2022-05-31
Category : Mathematics
ISBN : 9780471632054

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Spatial Analysis by John T. Kent,Kanti V. Mardia Pdf

SPATIAL ANALYSIS Explore the foundations and latest developments in spatial statistical analysis In Spatial Analysis, two distinguished authors deliver a practical and insightful exploration of the statistical investigation of the interdependence of random variables as a function of their spatial proximity. The book expertly blends theory and application, offering numerous worked examples and exercises at the end of each chapter. Increasingly relevant to fields as diverse as epidemiology, geography, geology, image analysis, and machine learning, spatial statistics is becoming more important to a wide range of specialists and professionals. The book includes: Thorough introduction to stationary random fields, intrinsic and generalized random fields, and stochastic models Comprehensive exploration of the estimation of spatial structure Practical discussion of kriging and the spatial linear model Spatial Analysis is an invaluable resource for advanced undergraduate and postgraduate students in statistics, data science, digital imaging, geostatistics, and agriculture. It’s also an accessible reference for professionals who are required to use spatial models in their work.

Spatial Statistics and Spatio-Temporal Data

Michael Sherman
Spatial Statistics and Spatio Temporal Data Book PDF

Author : Michael Sherman
Publisher : John Wiley & Sons
Page : 190 pages
File Size : 42,6 Mb
Release : 2011-01-06
Category : Mathematics
ISBN : 9780470974926

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Spatial Statistics and Spatio-Temporal Data by Michael Sherman Pdf

In the spatial or spatio-temporal context, specifying the correct covariance function is fundamental to obtain efficient predictions, and to understand the underlying physical process of interest. This book focuses on covariance and variogram functions, their role in prediction, and appropriate choice of these functions in applications. Both recent and more established methods are illustrated to assess many common assumptions on these functions, such as, isotropy, separability, symmetry, and intrinsic correlation. After an extensive introduction to spatial methodology, the book details the effects of common covariance assumptions and addresses methods to assess the appropriateness of such assumptions for various data structures. Key features: An extensive introduction to spatial methodology including a survey of spatial covariance functions and their use in spatial prediction (kriging) is given. Explores methodology for assessing the appropriateness of assumptions on covariance functions in the spatial, spatio-temporal, multivariate spatial, and point pattern settings. Provides illustrations of all methods based on data and simulation experiments to demonstrate all methodology and guide to proper usage of all methods. Presents a brief survey of spatial and spatio-temporal models, highlighting the Gaussian case and the binary data setting, along with the different methodologies for estimation and model fitting for these two data structures. Discusses models that allow for anisotropic and nonseparable behaviour in covariance functions in the spatial, spatio-temporal and multivariate settings. Gives an introduction to point pattern models, including testing for randomness, and fitting regular and clustered point patterns. The importance and assessment of isotropy of point patterns is detailed. Statisticians, researchers, and data analysts working with spatial and space-time data will benefit from this book as well as will graduate students with a background in basic statistics following courses in engineering, quantitative ecology or atmospheric science.