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Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources Sciences [Hardback]

(Department of Experimental Statistics at Louisiana State Univer), (Professor of Statistics at the University of Nebraska, Lincoln), , , , , (Professor and Interim Director of the Agricultural Statistics Laboratory at the University of Arkansas),
  • Formāts: Hardback, 304 pages, height x width x depth: 10x10x10 mm, weight: 454 g
  • Sērija : ASA, CSSA, and SSSA Books
  • Izdošanas datums: 13-Mar-2020
  • Izdevniecība: American Society of Agronomy
  • ISBN-10: 0891181822
  • ISBN-13: 9780891181828
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Analysis of Generalized Linear Mixed Models in the Agricultural and Natural Resources SciencesPalielināt
  • Formāts: Hardback, 304 pages, height x width x depth: 10x10x10 mm, weight: 454 g
  • Sērija : ASA, CSSA, and SSSA Books
  • Izdošanas datums: 13-Mar-2020
  • Izdevniecība: American Society of Agronomy
  • ISBN-10: 0891181822
  • ISBN-13: 9780891181828
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780891181828.html
Keywords:
The goal of this text for students and professionals is to help readers who have worked with linear mixed models make the transition to generalized linear mixed models. Benefits and challenges of generalized linear mixed models are discussed from a practitioner's perspective. SAS code is used to analyze examples in the agricultural and natural resources sciences. After a review of basic concepts, subsequent chapters cover generalized linear models, linear mixed models, generalized linear mixed models, more complex examples, and design of experiments. Readers should be familiar with standard techniques for normally distributed responses and have experience using SAS or other statistical software. The reader-friendly layout contains many boxes. Gbur directs the Agricultural Statistics Laboratory at the University of Arkansas. Annotation ©2012 Book News, Inc., Portland, OR (booknews.com)
Foreword vii
Preface ix
Authors xi
Conversion Factors for SI and Non-SI Units xiii
Chapter 1 Introduction
1(6)
1.1 Introduction
1(1)
1.2 Generalized Linear Mixed Models
2(1)
1.3 Historical Development
3(2)
1.4 Objectives of this Book
5(2)
Chapter 2 Background
7(28)
2.1 Introduction
7(1)
2.2 Distributions used in Generalized Linear Modeling
7(3)
2.3 Descriptions of the Distributions
10(5)
2.4 Likelihood Based Approach to Estimation
15(3)
2.5 Variations on Maximum Likelihood Estimation
18(1)
2.6 Likelihood Based Approach to Hypothesis Testing
19(3)
2.7 Computational Issues
22(2)
2.8 Fixed, Random, and Mixed Models
24(1)
2.9 The Design-Analysis of Variance-Generalized Linear Mixed Model Connection
25(5)
2.10 Conditional versus Marginal Models
30(1)
2.11 Software
30(5)
Chapter 3 Generalized Linear Models
35(24)
3.1 Introduction
35(2)
3.2 Inference in Generalized Linear Models
37(9)
3.3 Diognostics and Model Fit
46(6)
3.4 Generalized Linear Modeling versus Transformations
52(7)
Chapter 4 Linear Mixed Models
59(50)
4.1 Introduction
59(1)
4.2 Estimation and Inference in Linear Mixed Models
60(1)
4.3 Conditional and Marginal Models
61(6)
4.4 Split Plot Experiments
67(10)
4.5 Experiments Involving Repeated Measures
77(1)
4.6 Selection of a Covariance Model
78(2)
4.7 A Repeated Measures Example
80(8)
4.8 Analysis of Covariance
88(11)
4.9 Best Linear Unbiased Prediction
99(10)
Chapter 5 Generalized Linear Mixed Models
109(90)
5.1 Introduction
109(1)
5.2 Estimation and Inference in Generalized Linear Mixed Models
110(1)
5.3 Conditional and Marginal Models
111(14)
5.4 Three Simple Examples
125(24)
5.5 Over-Dispersion in Generalized Linear Mixed Models
149(2)
5.6 Over-Dispersion from an Incorrectly Specified Distribution
151(9)
5.7 Over-Dispersion from an Incorrect Linear Predictor
160(7)
5.8 Experiments Involving Repeated Measures
167(14)
5.9 Inference Issues for Repeated Measures Generalized Linear Mixed Models
181(3)
5.10 Multinomial Data
184(15)
Chapter 6 More Complex Examples
199(38)
6.1 Introduction
199(1)
6.2 Repeated Measures in Time and Space
199(11)
6.3 Analysis of a Precision Agriculture Experiment
210(27)
Chapter 7 Designing Experiments
237(34)
7.1 Introduction
237(1)
7.2 Power and Precision
238(1)
7.3 Power and Precision Analyses for Generalized Linear Mixed Models
239(2)
7.4 Methods of Determining Power and Precision
241(2)
7.5 Implementation of the Probability Distribution Method
243(7)
7.6 A Factorial Experiment with Different Design Options
250(5)
7.7 A Multi-location Experiment with a Binomial Response Variable
255(7)
7.8 A Split Plot Revisited with a Count as the Response Variable
262(6)
7.9 Summary and Conclusions
268(3)
Chapter 8 Parting Thoughts and Future Directions
271(6)
8.1 The Old Standard Statistical Practice
271(1)
8.2 The New Standard
272(2)
8.3 The Challenge to Adapt
274(3)
Index 277