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Model Based Geostatistics

Author: Peter Diggle
Publisher: Springer Science & Business Media
ISBN: 0387485368
Size: 31.37 MB
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This volume is the first book-length treatment of model-based geostatistics. The text is expository, emphasizing statistical methods and applications rather than the underlying mathematical theory. Analyses of datasets from a range of scientific contexts feature prominently, and simulations are used to illustrate theoretical results. Readers can reproduce most of the computational results in the book by using the authors' software package, geoR, whose usage is illustrated in a computation section at the end of each chapter. The book assumes a working knowledge of classical and Bayesian methods of inference, linear models, and generalized linear models.

Correlated Data Analysis Modeling Analytics And Applications

Author: Peter X. -K. Song
Publisher: Springer Science & Business Media
ISBN: 038771393X
Size: 59.57 MB
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This book covers recent developments in correlated data analysis. It utilizes the class of dispersion models as marginal components in the formulation of joint models for correlated data. This enables the book to cover a broader range of data types than the traditional generalized linear models. The reader is provided with a systematic treatment for the topic of estimating functions, and both generalized estimating equations (GEE) and quadratic inference functions (QIF) are studied as special cases. In addition to the discussions on marginal models and mixed-effects models, this book covers new topics on joint regression analysis based on Gaussian copulas.

Bayesian Computation With R

Author: Jim Albert
Publisher: Springer Science & Business Media
ISBN: 0387713859
Size: 22.92 MB
Format: PDF, ePub
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Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. Early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. Bayesian computational methods such as Laplace's method, rejection sampling, and the SIR algorithm are illustrated in the context of a random effects model. The construction and implementation of Markov Chain Monte Carlo (MCMC) methods is introduced. These simulation-based algorithms are implemented for a variety of Bayesian applications such as normal and binary response regression, hierarchical modeling, order-restricted inference, and robust modeling.

Advances And Challenges In Space Time Modelling Of Natural Events

Author: Emilio Porcu
Publisher: Springer Science & Business Media
ISBN: 3642170862
Size: 37.73 MB
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This book arises as the natural continuation of the International Spring School "Advances and Challenges in Space-Time modelling of Natural Events," which took place in Toledo (Spain) in March 2010. This Spring School above all focused on young researchers (Master students, PhD students and post-doctoral researchers) in academics, extra-university research and the industry who are interested in learning about recent developments, new methods and applications in spatial statistics and related areas, and in exchanging ideas and findings with colleagues.

Ecological Statistics

Author: Gordon A. Fox
Publisher: OUP Oxford
ISBN: 0191652881
Size: 32.56 MB
Format: PDF, Docs
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The application and interpretation of statistics are central to ecological study and practice. Ecologists are now asking more sophisticated questions than in the past. These new questions, together with the continued growth of computing power and the availability of new software, have created a new generation of statistical techniques. These have resulted in major recent developments in both our understanding and practice of ecological statistics. This novel book synthesizes a number of these changes, addressing key approaches and issues that tend to be overlooked in other books such as missing/censored data, correlation structure of data, heterogeneous data, and complex causal relationships. These issues characterize a large proportion of ecological data, but most ecologists' training in traditional statistics simply does not provide them with adequate preparation to handle the associated challenges. Uniquely, Ecological Statistics highlights the underlying links among many statistical approaches that attempt to tackle these issues. In particular, it gives readers an introduction to approaches to inference, likelihoods, generalized linear (mixed) models, spatially or phylogenetically-structured data, and data synthesis, with a strong emphasis on conceptual understanding and subsequent application to data analysis. Written by a team of practicing ecologists, mathematical explanations have been kept to the minimum necessary. This user-friendly textbook will be suitable for graduate students, researchers, and practitioners in the fields of ecology, evolution, environmental studies, and computational biology who are interested in updating their statistical tool kits. A companion web site provides example data sets and commented code in the R language.

Introduction To Empirical Processes And Semiparametric Inference

Author: Michael R. Kosorok
Publisher:
ISBN:
Size: 67.66 MB
Format: PDF, ePub, Mobi
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This book provides a self-contained, linear, and unified introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. The targeted audience includes statisticians, biostatisticians, and other researchers with a background in mathematical statistics who have an interest in learning about and doing research in empirical processes and semiparametric inference but who would like to have a friendly and gradual introduction to the area. The book can be used either as a research reference or as a textbook. The level of the book is suitable for a second year graduate course in statistics or biostatistics, provided the students have had a year of graduate level mathematical statistics and a semester of probability. The book consists of three parts. The first part is a concise overview of all of the main concepts covered in the book with a minimum of technicalities. The second and third parts cover the two respective main topics of empirical processes and semiparametric inference in depth. The connections between these two topics is also demonstrated and emphasized throughout the text. Each part has a final chapter with several case studies that use concrete examples to illustrate the concepts developed so far. The last two parts also each include a chapter which covers the needed mathematical preliminaries. Each main idea is introduced with a non-technical motivation, and examples are given throughout to illustrate important concepts. Homework problems are also included at the end of each chapter to help thereader gain additional insights.

Geostatistics Banff 2004

Author: Oy Leuangthong
Publisher: Springer Science & Business Media
ISBN: 9781402036101
Size: 55.32 MB
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The return of the congress to North America after 20 years of absence could not have been in a more ideal location. The beauty of Banff and the many offerings of the Rocky Mountains was the perfect background for a week of interesting and innovative discussions on the past, present and future of geostatistics. The congress was well attended with approximately 200 delegates from 19 countries across six continents. There was a broad spectrum of students and seasoned geostatisticians who shared their knowledge in many areas of study including mining, petroleum, and environmental applications. You will find 119 papers in this two volume set. All papers were presented at the congress and have been peer-reviewed. They are grouped by the different sessions that were held in Banff and are in the order of presentation. These papers provide a permanent record of different theoretical perspectives from the last four years. Not all of these ideas will stand the test of time and practice; however, their originality will endure. The practical applications in these proceedings provide nuggets of wisdom to those struggling to apply geostatistics in the best possible way. Students and practitioners will be digging through these papers for many years to come. Oy Leuangthong Clayton V. Deutsch ACKNOWLEDGMENTS We would like to thank the industry sponsors who contributed generously to the overall success and quality of the congress: De Beers Canada Earth Decision Sciences Maptek Chile Ltda. Mira Geoscience Nexen Inc. Petro-Canada Placer Dome Inc.

Spatial Statistics And Computational Methods

Author: Jesper Møller
Publisher: Springer Science & Business Media
ISBN: 0387218114
Size: 28.61 MB
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This volume shows how sophisticated spatial statistical and computational methods apply to a range of problems of increasing importance for applications in science and technology. It introduces topics of current interest in spatial and computational statistics, which should be accessible to postgraduate students as well as to experienced statistical researchers.