Gaussian Markov Random Fields by Havard Rue
Gaussian Markov Random Field (GMRF) models are most widely used in spatial statistics - a very active area of research in which few up-to-date reference works are available. This is the first book on the subject that provides a unified framework of GMRFs with particular emphasis on the computational aspects.This book includes extensive case-studies and, online, a c-library for fast and exact simulation. With chapters contributed by leading researchers in the field, this volume is essential reading for statisticians working in spatial theory and its applications, as well as quantitative researchers in a wide range of science fields where spatial data analysis is important.-
Statistical Inference
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Practical Risk Theory for Actuaries
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Analysis of Survival Data
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Generalized Linear Models with Random Effects
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An Introduction to the Bootstrap
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Queues
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Transformation and Weighting in Regression
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Sequential Analysis
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Asymptotic Analysis of Mixed Effects Models
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Statistics for Long-Memory Processes
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Analysis of Infectious Disease Data
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ROC Curves for Continuous Data
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Missing Data in Longitudinal Studies
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Analyzing and Modeling Rank Data
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Stochastic Geometry
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Semimartingales and their Statistical Inference
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Accelerated Life Models
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Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
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Quasi-Least Squares Regression
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Large Covariance and Autocovariance Matrices
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Design and Analysis of Cross-Over Trials
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Analysis of Variance for Functional Data
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Pareto Distributions
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Analysis of Incomplete Multivariate Data
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Simultaneous Inference in Regression
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Sufficient Dimension Reduction
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Markov Models & Optimization
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Multidimensional Scaling
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Biplots
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Analog Est Methods Econometric
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Measurement Error in Nonlinear Models
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Predictive Inference
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Subjective Probability Models for Lifetimes
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Smoothing Splines
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Bayesian Inference for Partially Identified Models
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Maximum Likelihood Estimation for Sample Surveys
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Mean Field Simulation for Monte Carlo Integration
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Robust Nonparametric Statistical Methods
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The Statistical Analysis of Multivariate Failure Time Data
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Measuring Statistical Evidence Using Relative Belief
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Sequential Change Detection and Hypothesis Testing
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Statistical Methods for Stochastic Differential Equations
"I thus enjoyed reading this book and I would recommend it to anyone involved in spatial modelling as a time-effective introduction to the field, including a concern for practical implementation that may be lacking elsewhere and a good stylistic balance between background and technicalities, between bases and illustrations that make it a rather easy reading"
– Christian P. Robert, Université Paris, in Statistics in Medicine, 2006, Vol. 25
Leonhard Held is a Full Professor of Biostatistics, Director of the Master's Program in Biostatistics and Chair of the Center for Reproducible Science at the University of Zurich, Switzerland. He has published several books and numerous articles on statistical methodology, applied statistics and biomedical research and teaches undergraduate and graduate-level courses in Biostatistics and Medical Statistics.
Daniel Sabanü¾Ž–”¼s Bovü¾Ž–”¼ completed his PhD in Statistics at the University of Zurich under the supervision of Leonhard Held. He started his career as a biostatistician in oncology drug development at Hoffmann-La Roche in 2013, and has been a data scientist at Google since 2018.
| SKU | Unavailable |
| ISBN 13 | 9781032477909 |
| ISBN 10 | 1032477903 |
| Title | Gaussian Markov Random Fields |
| Author | Havard Rue |
| Series | Chapman And Hall Crc Monographs On Statistics And Applied Probability |
| Condition | Unavailable |
| Binding Type | Paperback |
| Publisher | CRC Press LLC |
| Year published | 2023-01-21 |
| Number of pages | 280 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |