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Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, Diggle, Peter J.


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Автор: Diggle, Peter J.
Название:  Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
ISBN: 9781466560239
Издательство: Taylor&Francis
Классификация:

ISBN-10: 1466560231
Обложка/Формат: Hardback
Страницы: 268
Вес: 0.60 кг.
Дата издания: 23.07.2013
Серия: Chapman & hall/crc monographs on statistics and applied probability
Язык: English
Издание: 3 ed
Иллюстрации: 11 tables, black and white; 117 illustrations, black and white
Размер: 239 x 163 x 23
Читательская аудитория: General (us: trade)
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Поставляется из: Европейский союз


Statistical Analysis of Spatial and Spatio-Temporal Point Patterns

Автор: Diggle, Peter J.
Название: Statistical Analysis of Spatial and Spatio-Temporal Point Patterns
ISBN: 1032477474 ISBN-13(EAN): 9781032477473
Издательство: Taylor&Francis
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Цена: 7042.00 р.
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Statistical methods for spatio-temporal systems

Название: Statistical methods for spatio-temporal systems
ISBN: 0367390116 ISBN-13(EAN): 9780367390112
Издательство: Taylor&Francis
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Цена: 9798.00 р.
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Описание:

Statistical Methods for Spatio-Temporal Systems presents current statistical research issues on spatio-temporal data modeling and will promote advances in research and a greater understanding between the mechanistic and the statistical modeling communities.

Contributed by leading researchers in the field, each self-contained chapter starts with an introduction of the topic and progresses to recent research results. Presenting specific examples of epidemic data of bovine tuberculosis, gastroenteric disease, and the U.K. foot-and-mouth outbreak, the first chapter uses stochastic models, such as point process models, to provide the probabilistic backbone that facilitates statistical inference from data. The next chapter discusses the critical issue of modeling random growth objects in diverse biological systems, such as bacteria colonies, tumors, and plant populations. The subsequent chapter examines data transformation tools using examples from ecology and air quality data, followed by a chapter on space-time covariance functions. The contributors then describe stochastic and statistical models that are used to generate simulated rainfall sequences for hydrological use, such as flood risk assessment. The final chapter explores Gaussian Markov random field specifications and Bayesian computational inference via Gibbs sampling and Markov chain Monte Carlo, illustrating the methods with a variety of data examples, such as temperature surfaces, dioxin concentrations, ozone concentrations, and a well-established deterministic dynamical weather model.

Statistical Methods for Spatio-Temporal Systems

Автор: Finkenstadt, Barbel
Название: Statistical Methods for Spatio-Temporal Systems
ISBN: 1584885939 ISBN-13(EAN): 9781584885931
Издательство: Taylor&Francis
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Цена: 22202.00 р.
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Spatio-Temporal Methods in Environmental Epidemiology

Автор: Shaddick, Gavin
Название: Spatio-Temporal Methods in Environmental Epidemiology
ISBN: 0367783460 ISBN-13(EAN): 9780367783464
Издательство: Taylor&Francis
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Цена: 6583.00 р.
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Spatial and Spatio-Temporal Geostatistical Modeling and Kriging

Автор: Fernandez-Avile G., Montero Jos? M., Fern?ndez-Avil? G.
Название: Spatial and Spatio-Temporal Geostatistical Modeling and Kriging
ISBN: 1118413180 ISBN-13(EAN): 9781118413180
Издательство: Wiley
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Цена: 11238.00 р.
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Описание: Statistical Methods for Spatial and Spatio-Temporal Data Analysis provides a complete range of spatio-temporal covariance functions and discusses ways of constructing them. This book is a unified approach to modeling spatial and spatio-temporal data together with significant developments in statistical methodology with applications in R.

Using R for Bayesian Spatial and Spatio-Temporal Health Modeling

Автор: Lawson, Andrew B.
Название: Using R for Bayesian Spatial and Spatio-Temporal Health Modeling
ISBN: 0367760673 ISBN-13(EAN): 9780367760670
Издательство: Taylor&Francis
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Цена: 7042.00 р.
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Using r for bayesian spatial and spatio-temporal health modeling

Автор: Lawson, Andrew B.
Название: Using r for bayesian spatial and spatio-temporal health modeling
ISBN: 0367490129 ISBN-13(EAN): 9780367490126
Издательство: Taylor&Francis
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Цена: 17609.00 р.
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Описание: Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies.

Modeling Conflict Dynamics with Spatio-temporal Data

Автор: Andrew Zammit-Mangion; Michael Dewar; Visakan Kadi
Название: Modeling Conflict Dynamics with Spatio-temporal Data
ISBN: 3319010379 ISBN-13(EAN): 9783319010373
Издательство: Springer
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Цена: 7182.00 р.
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Описание: The book also demonstrates the methods on the WikiLeaks Afghan War Diary, the results showing that this approach allows deeper insights into conflict dynamics and allows a strikingly statistically accurate forward prediction of armed opposition group activity in 2010, based solely on data from preceding years.

Bayesian Modeling of Spatio-Temporal Data with R

Автор: Sahu Sujit
Название: Bayesian Modeling of Spatio-Temporal Data with R
ISBN: 0367277980 ISBN-13(EAN): 9780367277987
Издательство: Taylor&Francis
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Цена: 15004.00 р.
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Описание: This book is designed to make spatio-temporal modeling and analysis understandable to students and researchers, mathematicians and statisticians and practitioners in the applied sciences. By avoiding hardcore math and calculus, this book aims to be a bridge that removes the statistical knowledge gap from among the applied scientists.

Spatio-temporal statistics with r

Автор: Wikle, Christopher K. (university Of Missouri, Columbia, Usa) Zammit-mangion, Andrew Cressie, Noel
Название: Spatio-temporal statistics with r
ISBN: 1138711136 ISBN-13(EAN): 9781138711136
Издательство: Taylor&Francis
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Цена: 7961.00 р.
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Spatial Point Patterns

Автор: Baddeley
Название: Spatial Point Patterns
ISBN: 1482210207 ISBN-13(EAN): 9781482210200
Издательство: Taylor&Francis
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Цена: 15004.00 р.
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Описание:

Modern Statistical Methodology and Software for Analyzing Spatial Point Patterns

Spatial Point Patterns: Methodology and Applications with R shows scientific researchers and applied statisticians from a wide range of fields how to analyze their spatial point pattern data. Making the techniques accessible to non-mathematicians, the authors draw on their 25 years of software development experiences, methodological research, and broad scientific collaborations to deliver a book that clearly and succinctly explains concepts and addresses real scientific questions.

Practical Advice on Data Analysis and Guidance on the Validity and Applicability of Methods

The first part of the book gives an introduction to R software, advice about collecting data, information about handling and manipulating data, and an accessible introduction to the basic concepts of point processes. The second part presents tools for exploratory data analysis, including non-parametric estimation of intensity, correlation, and spacing properties. The third part discusses model-fitting and statistical inference for point patterns. The final part describes point patterns with additional "structure," such as complicated marks, space-time observations, three- and higher-dimensional spaces, replicated observations, and point patterns constrained to a network of lines.

Easily Analyze Your Own Data

Throughout the book, the authors use their spatstat package, which is free, open-source code written in the R language. This package provides a wide range of capabilities for spatial point pattern data, from basic data handling to advanced analytic tools. The book focuses on practical needs from the user's perspective, offering answers to the most frequently asked questions in each chapter.

Spectral Analysis for Univariate Time Series

Автор: Donald B. Percival, Andrew T. Walden
Название: Spectral Analysis for Univariate Time Series
ISBN: 1107028140 ISBN-13(EAN): 9781107028142
Издательство: Cambridge Academ
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Цена: 14573.00 р.
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Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.


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