Contemporary Statistical Models for the Plant and Soil Sciences

ISBN-10: 1584881119

ISBN-13: 9781584881117

Edition: 2001

List price: $167.95
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Description: 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 why a particular method works and how it is put in to practice.About the CD-ROMThe accompanying CD-ROM is 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.

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Book details

List price: $167.95
Copyright year: 2001
Publisher: CRC Press LLC
Publication date: 11/13/2001
Binding: Hardcover
Pages: 760
Size: 7.00" wide x 9.75" long x 1.75" tall
Weight: 3.432
Language: English

Preface
About the Authors
The CD-ROM
Statistical Models
Mathematical and Statistical Models
Functional Aspects of Models
The Inferential Steps--Estimation and Testing
t-Tests in Terms of Statistical Models
Embedding Hypotheses
Hypothesis and Significance Testing--Interpretation of the p-Value
Classes of Statistical Models
The Basic Component Equation
Linear and Nonlinear Models
Regression and Analysis of Variance Models
Univariate and Multivariate Models
Fixed, Random, and Mixed Effects Models
Generalized Linear Models
Errors in Variable Models
Data Structures
Introduction
Classification by Response Type
Classification by Study Type
Clustered Data
Clustering through Hierarchical Random Processes
Clustering through Repeated Measurements
Autocorrelated Data
The Autocorrelation Function
Consequences of Ignoring Autocorrelation
Autocorrelation in Designed Experiments
From Independent to Spatial Data--a Progression of Clustering
Linear Algebra Tools
Introduction
Matrices and Vectors
Basic Matrix Operations
Matrix Inversion--Regular and Generalized Inverse
Mean, Variance, and Covariance of Random Vectors
The Trace and Expectation of Quadratic Forms
The Multivariate Gaussian Distribution
Matrix and Vector Differentiation
Using Matrix Algebra to Specify Models
Linear Models
Nonlinear Models
Variance-Covariance Matrices and Clustering
The Classical Linear Model: Least Squares and Alternatives
Introduction
Least Squares Estimation and Partitioning of Variation
The Principle
Partitioning Variability through Sums of Squares
Sequential and Partial Sums of Squares and the Sum of Squares Reduction Test
Factorial Classification
The Means and Effects Model
Effect Types in Factorial Designs
Sum of Squares Partitioning through Contrasts
Effects and Contrasts in The SAS System
Diagnosing Regression Models
Residual Analysis
Recursive and Linearly Recovered Errors
Case Deletion Diagnostics
Collinearity Diagnostics
Ridge Regression to Combat Collinearity
Diagnosing Classification Models
What Matters?
Diagnosing and Combating Heteroscedasticity
Median Polishing of Two-Way Layouts
Robust Estimation
L[subscript 1]-Estimation
M-Estimation
Robust Regression for Prediction Efficiency Data
M-Estimation in Classification Models
Nonparametric Regression
Local Averaging and Local Regression
Choosing the Smoothing Parameter
Appendix A on CD-ROM
Mathematical Details
Least Squares
Hypothesis Testing in the Classical Linear Model
Diagnostics in Regression Models
Ridge Regression
L[subscript 1]-Estimation
M-Estimation
Nonparametric Regression
Nonlinear Models
Introduction
Models as Laws or Tools
Linear Polynomials Approximate Nonlinear Models
Fitting a Nonlinear Model to Data
Estimating the Parameters
Tracking Convergence
Starting Values
Goodness-of-Fit
Hypothesis Tests and Confidence Intervals
Testing the Linear Hypothesis
Confidence and Prediction Intervals
Transformations
Transformation to Linearity
Transformation to Stabilize the Variance
Parameterization of Nonlinear Models
Intrinsic and Parameter-Effects Curvature
Reparameterization through Defining Relationships
Applications
Basic Nonlinear Analysis with The SAS System--Mitscherlich's Yield Equation
The Sampling Distribution of Nonlinear Estimators--the Mitscherlich Equation Revisited
Linear-Plateau Models and Their Relatives--a Study of Corn Yields from Tennessee
Critical NO[subscript 3] Concentrations as a Function of Sampling Depth--Comparing Join-Points in Plateau Models
Factorial Treatment Structure with Nonlinear Response
Modeling Hormetic Dose Response through Switching Functions
Modeling a Yield-Density Relationship
Weighted Nonlinear Least Squares Analysis with Heteroscedastic Errors
Appendix A on CD-ROM
Forms of Nonlinear Models
Concave and Convex Models, Yield-Density Models
Models with Sigmoidal Shape, Growth Models
Mathematical Details
Taylor Series Involving Vectors
Nonlinear Least Squares and the Gauss-Newton Algorithm
Nonlinear Generalized Least Squares
The Newton-Raphson Algorithm
Convergence Criteria
Hypothesis Testing, Confidence and Prediction Intervals
Generalized Linear Models
Introduction
Components of a Generalized Linear Model
Random Component
Systematic Component and Link Function
Generalized Linear Models in The SAS System
Grouped and Ungrouped Data
Parameter Estimation and Inference
Solving the Likelihood Problem
Testing Hypotheses about Parameters and Their Functions
Deviance and Pearson's X[superscript 2] Statistic
Testing Hypotheses through Deviance Partitioning
Generalized R[superscript 2] Measures of Goodness-of-Fit
Modeling an Ordinal Response
Cumulative Link Models
Software Implementation and Example
Overdispersion
Applications
Dose-Response and LD[subscript 50] Estimation in a Logistic Regression Model
Binomial Proportions in a Randomized Block Design--the Hessian Fly Experiment
Gamma Regression and Yield Density Models
Effects of Judges' Experience on Bean Canning Quality Ratings
Ordinal Ratings in a Designed Experiment with Factorial Treatment Structure and Repeated Measures
Log-Linear Modeling of Rater Agreement
Modeling the Sample Variance of Scab Infection
A Poisson/Gamma Mixing Model for Overdispersed Poppy Counts
Appendix A on CD-ROM
Mathematical Details and Special Topics
Exponential Family of Distributions
Maximum Likelihood Estimation
Iteratively Reweighted Least Squares
Hypothesis Testing
Fieller's Theorem and the Variance of a Ratio
Overdispersion Mechanisms for Counts
Linear Mixed Models for Clustered Data
Introduction
The Laird-Ware Model
Rationale
The Two-Stage Concept
Fixed or Random Effects
Choosing the Inference Space
Estimation and Inference
Maximum and Restricted Maximum Likelihood
Estimated Generalized Least Squares
Hypothesis Testing
Correlations in Mixed Models
Induced Correlations and the Direct Approach
Within-Cluster Correlation Models
Split-Plots, Repeated Measures, and the Huynh-Feldt Conditions
Applications
Two-Stage Modeling of Apple Growth over Time
On-Farm Experimentation with Randomly Selected Farms
Nested Errors through Subsampling
Recovery of Inter-Block Information in Incomplete Block Designs
A Split-Strip-Plot Experiment for Soybean Yield
Repeated Measures in a Completely Randomized Design
A Longitudinal Study of Water Usage in Horticultural Trees
Cumulative Growth of Muskmelons in Subsampling Design
Appendix A on CD-ROM
Mathematical Details and Special Topics
Henderson's Mixed Model Equations
Solutions to the Mixed Model Equations
Likelihood Based Estimation
Estimated Generalized Least Squares Estimation
Hypothesis Testing
The First-Order Autoregressive Model
Nonlinear Models for Clustered Data
Introduction
Nonlinear and Generalized Linear Mixed Models
Toward an Approximate Objective Function
Three Linearizations
Linearization in Generalized Linear Mixed Models
Integral Approximation Methods
Applications
A Nonlinear Mixed Model for Cumulative Tree Bole Volume
Poppy Counts Revisited--a Generalized Linear Mixed Model for Overdispersed Count Data
Repeated Measures with an Ordinal Response
Appendix A on CD-ROM
Mathematical Details and Special Topics
PA and SS Linearizations
Generalized Estimating Equations
Linearization in Generalized Linear Mixed Models
Gaussian Quadrature
Statistical Models for Spatial Data
Changing the Mindset
Samples of Size One
Random Functions and Random Fields
Types of Spatial Data
Stationarity and Isotropy--the Built-in Replication Mechanism of Random Fields
Semivariogram Analysis and Estimation
Elements of the Semivariogram
Parametric Isotropic Semivariogram Models
The Degree of Spatial Continuity (Structure)
Semivariogram Estimation and Fitting
The Spatial Model
Spatial Prediction and the Kriging Paradigm
Motivation of the Prediction Problem
The Concept of Optimal Prediction
Ordinary and Universal Kriging
Some Notes on Kriging
Extensions to Multiple Attributes
Spatial Regression and Classification Models
Random Field Linear Models
Some Philosophical Considerations
Parameter Estimation
Autoregressive Models for Lattice Data
The Neighborhood Structure
First-Order Simultaneous and Conditional Models
Parameter Estimation
Choosing the Neighborhood Structure
Analyzing Mapped Spatial Point Patterns
Introduction
Random, Aggregated, and Regular Patterns--the Notion of Complete Spatial Randomness
Testing the CSR Hypothesis in Mapped Point Patterns
Second-Order Properties of Point Patterns
Applications
Exploratory Tools for Spatial Data--Diagnosing Spatial Autocorrelation with Moran's I
Modeling the Semivariogram of Soil Carbon
Spatial Prediction--Kriging of Lead Concentrations
Spatial Random Field Models--Comparing C/N Ratios among Tillage Treatments
Spatial Random Field Models--Spatial Regression of Soil Carbon on Soil N
Spatial Generalized Linear Models--Spatial Trends in the Hessian Fly Experiment
Simultaneous Spatial Autoregression--Modeling Wiebe's Wheat Yield Data
Point Patterns--First- and Second-Order Properties of a Mapped Pattern
Appendix A on CD-ROM
Mathematical Details and Special Topics
Geostatistical Data
Estimating the Empirical Semivariogram
Parametric Fitting of the Semivariogram
Nonparametric Fitting of the Semivariogram
Solutions to Kriging Equations
Is Kriging Perfect Interpolation?
Block and Indicator Kriging
Spatial Random Field Models
Composite Likelihood in Spatial Generalized Linear Models
Lattice Data
Maximum Likelihood Estimation in Lattice Models
Are Autoregressive Models Stationary?
Point Patterns
Estimating First- and Second-Order Properties of Point Patterns
Point Process Models
Spectral Analysis of Spatial Point Patterns
Supplementary Application
Point Patterns--Spectral Analysis of Seedling Counts
Bibliography
Author Index
Subject Index
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