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Product Details  

• Hardcover: 738 pages
• Publisher: CRC Press; 1 edition (November 13, 2001)
• Language: English

Table of Content

Statistical Models Mathematical and Statistical Models Functional Aspects of Models The Inferential Steps ó Estimation and Testing t-Tests in Terms of Statistical Models Embedding Hypotheses Hypothesis and Significance Testing ó Interpretation of the p-Value Classes of Statistical Models Data Structures Introduction Classification by Response Type Classification by Study Type Clustered Data Autocorrelated Data From Independent to Spatial Data ó A Progression of Clustering Linear Algebra Tools Introduction Matrices and Vectors Basic Matrix Operations Matrix Inversion ó 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 The Classical Linear Model: Least Squares and Alternatives Introduction Least Squares Estimation and Partitioning of Variation Factorial Classification Diagnosing Regression Models Diagnosing Classification Models Robust Estimation Nonparametric Regression Nonlinear Models Introduction Models as Laws or Tools Linear Polynomials Approximate Nonlinear Models Fitting a Nonlinear Model to Data Hypothesis Tests and Confidence Intervals Transformations Parameterization of Nonlinear Models Applications Generalized Linear Models Introduction Components of a Generalized Linear Model Grouped and Ungrouped Data Parameter Estimation and Inference Modeling an Ordinal Response Overdispersion Applications Linear Mixed Models for Clustered Data Introduction The Laird-Ware Model Choosing the Inference Space Estimation and Inference Correlations in Mixed Models Applications Nonlinear Models for Clustered Data Introduction Nonlinear and Generalized Linear Mixed Models Towards an Approximate Objective Function Applications Statistical Models for Spatial Data Changing the Mindset Semivariogram Analysis and Estimation The Spatial Model Spatial Prediction and the Kriging Paradigm Spatial Regression and Classification Models Autoregressive Models for Lattice Data Analyzing Mapped Spatial Point Patterns Applications Bibliography

Editorial Reviews                                                                                                                          "This text [presents] many of the newer statistical modeling techniques for data analysis using examples familiar to plant and soil scientists…keeping the mathematical complexity to a minimum. I applaud the authors for their efforts to bring the current state of the area of statistical modeling into the realm of the plant and soil sciences." --Clarence E. Watson, Experimental Statistics and Plant and Soil Sciences, Mississippi State University, USA "My overall impression is that it is a superbly crafted text replete with many carefully chosen examples that instructively demonstrate contemporary models and modelling practices. The authors' attention to fine detail in the presentation of materials is evident in every chapter. Researchers, instructors, and students alike doubtlessly will find the snippets of SAS code and specially tailored macros to be of immense value when fitting data to the contemporary models described in this treatise." --Timothy Gregoire, School of Forestry and Environmental Studies, Yale University, New Haven , USA