Research Catalog

Title

Contemporary statistical models for the plant and soil sciences / Oliver Schabenberger, Francis J. Pierce.

Author

Schabenberger, Oliver.

Publication

Boca Raton, Fla. : CRC Press, [2002], ©2002.

Items in the Library & Off-site

Details

Additional Authors

Pierce, F. J. (Francis J.)

Description

xxii, 738 pages : illustrations; 27 cm +

Subjects

• Plants, Cultivated > Statistical methods
• Soil science > Statistical methods

Bibliography (note)

• Includes bibliographical references (p. 703-720) and indexes.

System Details (note)

• System requirements: Windows 95/98/NT/2000.

Contents

• 1. Statistical Models. 1.1. Mathematical and Statistical Models. 1.2. Functional Aspects of Models. 1.3. The Inferential Steps - Estimation and Testing. 1.4. t-Tests in Terms of Statistical Models. 1.5. Embedding Hypotheses. 1.6. Hypothesis and Significance Testing - Interpretation of the p-Value. 1.7. Classes of Statistical Models -- 2. Data Structure. 2.1. Introduction. 2.2. Classification by Response Type. 2.3. Classification by Study Type. 2.4. Clustered Data. 2.5. Autocorrelated Data. 2.6. From Independent to Spatial Data - a Progression of Clustering -- 3. Linear Algebra Tools. 3.1. Introduction. 3.2. Matrices and Vectors. 3.3. Basic Matrix Operations. 3.4. Matrix Inversion - Regular and Generalized Inverse. 3.5. Mean, Variance, and Covariance of Random Vectors. 3.6. The Trace and Expectation of Quadratic Forms. 3.7. The Multivariate Gaussian Distribution. 3.8. Matrix and Vector Differentiation. 3.9. Using Matrix Algebra to Specify Models --
• 4. The Classical Linear Model: Least Squares and Alternatives. 4.1. Introduction. 4.2. Least Squares Estimation and Partitioning of Variation. 4.3. Factorial Classification. 4.4. Diagnosing Regression Models. 4.5. Diagnosing Classification Models. 4.6. Robust Estimation. 4.7. Nonparametric Regression -- 5. Nonlinear Models. 5.1. Introduction. 5.2. Models as Laws or Tools. 5.3. Linear Polynomials Approximate Nonlinear Models. 5.4. Fitting a Nonlinear Model to Data. 5.5. Hypothesis Tests and Confidence Intervals. 5.6. Transformations. 5.7. Parameterization of Nonlinear Models. 5.8. Applications -- 6. Generalized Linear Models. 6.1. Introduction. 6.2. Components of a Generalized Linear Model. 6.3. Grouped and Ungrouped Data. 6.4. Parameter Estimation and Inference. 6.5. Modeling an Ordinal Response. 6.6. Overdispersion. 6.7. Applications -- 7. Linear Mixed Models for Clustered Data. 7.1. Introduction. 7.2. The Laird-Ware Model.
• 7.3. Choosing the Inference Space. 7.4. Estimation and Inference. 7.5. Correlations in Mixed Models. 7.6. Applications -- 8. Nonlinear Models for Clustered Data. 8.1. Introduction. 8.2. Nonlinear and Generalized Linear Mixed Models. 8.3. Toward an Approximate Objective Function. 8.4. Applications -- 9. Statistical Models for Spatial Data. 9.1. Changing the Mindset. 9.2. Semivariogram Analysis and Estimation. 9.3. The Spatial Model. 9.4. Spatial Prediction and the Kriging Paradigm. 9.5. Spatial Regression and Classification Models. 9.6. Autoregressive Models for Lattice Data. 9.7. Analyzing Mapped Spatial Point Patterns. 9.8. Applications.

ISBN

1584881119 (alk. paper)

LCCN

2001043254

OCLC

• ocm47716435
• SCSB-4222247

Owning Institutions

Columbia University Libraries