Linear Model Theory With Examples and Exercises
This textbook presents a unified and rigorous approach to best linear unbiased estimation and prediction of parameters and random quantities in linear models, as well as other theory upon which much of the statistical methodology associated with linear models is based. The single most unique feature...
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| Format: | eBook |
| Language: | English |
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Cham
Springer International Publishing
2020, 2020
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| Edition: | 1st ed. 2020 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Preface
- 1 A Brief Introduction
- 2 Selected Matrix Algebra Topics and Results
- 3 Generalized Inverses and Solutions to Systems of Linear Equations
- 4 Moments of a Random Vector and of Linear and Quadratic Forms in a Random Vector
- 5 Types of Linear Models
- 6 Estimability
- 7 Least Squares Estimation for the Gauss-Markov Model
- 8 Least Squares Geometry and the Overall ANOVA
- 9 Least Squares Estimation and ANOVA for Partitioned Models
- 10 Constrained Least Squares Estimation and ANOVA
- 11 Best Linear Unbiased Estimation for the Aitken Model
- 12 Model Misspecification
- 13 Best Linear Unbiased Prediction
- 14 Distribution Theory
- 15 Inference for Estimable and Predictable Functions
- 16 Inference for Variance-Covariance Parameters
- 17 Empirical BLUE and BLUP
- Index