-PDF Download- Contemporary Statistical Models For The Plant And Soil Sciences EBOOK

Contemporary Statistical Models for the Plant and Soil Sciences

Author: Oliver Schabenberger

Publisher: CRC Press

Published: 2001-11-13

Total Pages: 762

ISBN-13: 1420040197

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.

Biogeochemistry of Wetlands

Author: K. Ramesh Reddy

Publisher: CRC Press

Published: 2008-07-28

Total Pages: 800

ISBN-13: 0203491459

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Wetland ecosystems maintain a fragile balance of soil, water, plant, and atmospheric components in order to regulate water flow, flooding, and water quality. Marginally covered in traditional texts on biogeochemistry or on wetland soils, Biogeochemistry of Wetlands is the first to focus entirely on the biological, geological, physical, and chemical

Methods of Statistical Model Estimation

Author: Joseph M. Hilbe

Publisher: CRC Press

Published: 2013-05-28

Total Pages: 257

ISBN-13: 1439858020

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Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for statistical model fitting. The text presents algorithms for the estimation of a variety of regression procedures using maximum likelihood estimation, iteratively reweighted least squares regression, the EM algorithm, and MCMC sampling. Fully developed, working R code is constructed for each method. The book starts with OLS regression and generalized linear models, building to two-parameter maximum likelihood models for both pooled and panel models. It then covers a random effects model estimated using the EM algorithm and concludes with a Bayesian Poisson model using Metropolis-Hastings sampling. The book's coverage is innovative in several ways. First, the authors use executable computer code to present and connect the theoretical content. Therefore, code is written for clarity of exposition rather than stability or speed of execution. Second, the book focuses on the performance of statistical estimation and downplays algebraic niceties. In both senses, this book is written for people who wish to fit statistical models and understand them. See Professor Hilbe discuss the book.

Spatial Data Analysis in Ecology and Agriculture Using R

Author: Richard E. Plant

Publisher: CRC Press

Published: 2012-03-07

Total Pages: 637

ISBN-13: 1439819149

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Assuming no prior knowledge of R, Spatial Data Analysis in Ecology and Agriculture Using R provides practical instruction on the use of the R programming language to analyze spatial data arising from research in ecology and agriculture. Written in terms of four data sets easily accessible online, this book guides the reader through the analysis of each data set, including setting research objectives, designing the sampling plan, data quality control, exploratory and confirmatory data analysis, and drawing scientific conclusions. Based on the author’s spatial data analysis course at the University of California, Davis, the book is intended for classroom use or self-study by graduate students and researchers in ecology, geography, and agricultural science with an interest in the analysis of spatial data.

Generalized Linear Mixed Models

Author: Walter W. Stroup

Publisher: CRC Press

Published: 2016-04-19

Total Pages: 547

ISBN-13: 1439815135

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With numerous examples using SAS PROC GLIMMIX, this text presents an introduction to linear modeling using the generalized linear mixed model as an overarching conceptual framework. For readers new to linear models, the book helps them see the big picture. It shows how linear models fit with the rest of the core statistics curriculum and points out the major issues that statistical modelers must consider.

Handbook of Spatial Statistics

Author: Alan E. Gelfand

Publisher: CRC Press

Published: 2010-03-19

Total Pages: 622

ISBN-13: 1420072889

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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

Encyclopedia of Forest Sciences

Author: Julian Evans

Publisher: Academic Press

Published: 2004-04-02

Total Pages: 6502

ISBN-13: 0080548016

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A combination of broad disciplinary coverage and scientific excellence, the Encyclopedia of Forest Sciences will be an indispensable addition to the library of anyone interested in forests, forestry and forest sciences. Packed with valuable insights from experts all over the world, this remarkable set not only summarizes recent advances in forest science techniques, but also thoroughly covers the basic information vital to comprehensive understanding of the important elements of forestry. The Encyclopedia of Forest Sciences also covers relevant biology and ecology, different types of forestry (e.g. tropical forestry and dryland forestry), scientific names of trees and shrubs, and the applied, economic, and social aspects of forest management. Valuable key features further enhance the utility of this Encyclopedia as an exceptional reference tool. Also available online via ScienceDirect – featuring extensive browsing, searching, and internal cross-referencing between articles in the work, plus dynamic linking to journal articles and abstract databases, making navigation flexible and easy. For more information, pricing options and availability visit www.info.sciencedirect.com. Edited and written by a distinguished group of editors and contributors Well-organized encyclopedic format provides concise, readable entries, easy searches, and thorough cross-references Illustrative tables, figures, and photographs in every entry, produced in full color Comprehensive glossary defines new and important terms Complete, up-to-date coverage of over 60 areas of forest sciences - sure to be of interest to scientists, students, and professionals alike! Editor-in-Chief is the past president of the International Union of Forestry Research Organizations, the oldest international collaborative forestry research organization with over 15,000 scientists from 100 countries

SAS for Mixed Models

Author: Walter W. Stroup

Publisher: SAS Institute

Published: 2018-12-12

Total Pages: 608

ISBN-13: 163526152X

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Discover the power of mixed models with SAS. Mixed models—now the mainstream vehicle for analyzing most research data—are part of the core curriculum in most master’s degree programs in statistics and data science. In a single volume, this book updates both SAS® for Linear Models, Fourth Edition, and SAS® for Mixed Models, Second Edition, covering the latest capabilities for a variety of applications featuring the SAS GLIMMIX and MIXED procedures. Written for instructors of statistics, graduate students, scientists, statisticians in business or government, and other decision makers, SAS® for Mixed Models is the perfect entry for those with a background in two-way analysis of variance, regression, and intermediate-level use of SAS. This book expands coverage of mixed models for non-normal data and mixed-model-based precision and power analysis, including the following topics: Random-effect-only and random-coefficients models Multilevel, split-plot, multilocation, and repeated measures models Hierarchical models with nested random effects Analysis of covariance models Generalized linear mixed models This book is part of the SAS Press program.

Working with Dynamic Crop Models

Author: Daniel Wallach

Publisher: Academic Press

Published: 2013-11-25

Total Pages: 504

ISBN-13: 0444594469

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This second edition of Working with Dynamic Crop Models is meant for self-learning by researchers or for use in graduate level courses devoted to methods for working with dynamic models in crop, agricultural, and related sciences. Each chapter focuses on a particular topic and includes an introduction, a detailed explanation of the available methods, applications of the methods to one or two simple models that are followed throughout the book, real-life examples of the methods from literature, and finally a section detailing implementation of the methods using the R programming language. The consistent use of R makes this book immediately and directly applicable to scientists seeking to develop models quickly and effectively, and the selected examples ensure broad appeal to scientists in various disciplines. New to this edition: 50% new content – 100% reviewed and updated Clearly explains practical application of the methods presented, including R language examples Presents real-life examples of core crop modeling methods, and ones that are translatable to dynamic system models in other fields

Modern Methodology and Applications in Spatial-Temporal Modeling

Author: Gareth William Peters

Publisher: Springer

Published: 2016-01-08

Total Pages: 111

ISBN-13: 4431553398

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This book provides a modern introductory tutorial on specialized methodological and applied aspects of spatial and temporal modeling. The areas covered involve a range of topics which reflect the diversity of this domain of research across a number of quantitative disciplines. For instance, the first chapter deals with non-parametric Bayesian inference via a recently developed framework known as kernel mean embedding which has had a significant influence in machine learning disciplines. The second chapter takes up non-parametric statistical methods for spatial field reconstruction and exceedance probability estimation based on Gaussian process-based models in the context of wireless sensor network data. The third chapter presents signal-processing methods applied to acoustic mood analysis based on music signal analysis. The fourth chapter covers models that are applicable to time series modeling in the domain of speech and language processing. This includes aspects of factor analysis, independent component analysis in an unsupervised learning setting. The chapter moves on to include more advanced topics on generalized latent variable topic models based on hierarchical Dirichlet processes which recently have been developed in non-parametric Bayesian literature. The final chapter discusses aspects of dependence modeling, primarily focusing on the role of extreme tail-dependence modeling, copulas, and their role in wireless communications system models.