Robert, C. P. ed.
The Bayesian Choice 2nd ed.
From Decision-Theoretic Foundations to Computational Implementation
Springer-Verlag 2007.8
577 pp.(P) * Now in paper edition
ISBN 0-387-71598-3
6,400円
Contents
1.Decision-theoretic foundations of statistical inference/ 2.From prior information to prior distributions/ 3.Bayesian point estimation/ 4.Tests and confidence regions/ 5.Bayesian Calculations/ 6.Model Choice/ 7.Admissibility and complete classes/ 8.Invariance, Haar measures, and equivariant estimators/ 9.Hierarchical and empirical Bayes extensions/ 10.A defense of the Bayesian choice/ Index/ *
Albert, J.
Bayesian Computation with R
Springer-Verlag 2007.8
270 pp.(P)
ISBN 0-387-71384-0
6,400円
Contents
1.An introduction to R/ 2.Introduction to Bayesian thinking/ 3.Single parameter models/ 4. Multiparameter models/ 5.Introduction to Bayesian computation/ 6.Markov chain Monte Carlo methods/ 7.Hierarchical modeling/ 8.Model comparision/ 9.Regression models/ 10.Gibbs sampling/ 11.Using R to interface with WinBUGS/ Index/ * Bayesian Computation with R introduces Bayesian modeling by the use of computation using the R language. The early chapters present the basic tenets of Bayesian thinking by use of familiar one and two-parameter inferential problems. *
Van Trees, H. L. & Bell, K. L. ed.
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
パラメータ推定、非線形フィルタリングとトラッキングのベイジアン境界
John Wiley & Sons 2007.8
951 pp. (H)
ISBN 0-470-12095-9
15,400円
Contents
Preface / Introduction / 1 Bayesian Estimation: Static Parameters / 2 Bayesian Estimation: Random Processes / 3 Outline of the Book / Part I Bayesian Cram r Rao Bounds / Part II Global Bayesian Bounds / Part III Hybrid Bayesian Bounds / Part IV Constrained Cram r Rao Bounds / Part V Applications: Static Parameters / Part VI Nonlinear Stochastic Dynamic Systems / Part VII Applications: Nonlinear Dynamic Systems / Part VIII Statistical Literature / References / Author Index / *
* Bayesian Bounds provides a collection of the important papers dealing with the theory and application of Bayesian bounds. The book is essential to both engineers and statisticians whether they are practitioners or theorists. Each part of the book is introduced with the contributions of each selected paper and their interrelationship. *
Stauffer , H. B.
Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists
天然資源科学者のベイズ統計法とFrequentist統計法
John Wiley & Sons 2007.12
400 pp.(H)
ISBN 0-470-16504-9
12,000円
Contents
1. Introduction/ 2. Bayesian Statistical Analysis I: Introduction/ 3. Bayesian Statistical Inference II: Bayesian Hypothesis Testing and Decision theory/ 4. Bayesian Statistical Inference III: MCMC Algorithms and WinBUGS Software Applications/ 5. Alternative Strategies for Model Selection and Inference Using Information-Theoretic Criteria/ 6. An Introduction to Generalized Linear Models: Logistic Regression Models/ 7. Introduction to Mixed-Effects Modeling/ 8. Summary and Conclusions/ Appendix A. review of Linear regression and Multiple Linear regression Analysis/ Appendix B. Answers to Problems/ References/ Index/
* Beginning with case studies illustrating important problems in natural resource science, the book goes on to describe the contemporary statistical methods that provide solutions to these case studies. This book also compares the advantages of Bayesian statistical analysis with the standard traditional frequentist approach. *
Seber, G. A. F.
A Matrix Handbook for Statisticians
統計者のためのマトリクスハンドブック
John Wiley & Sons 2007.11
559 pp. (H)
ISBN 0-471-74869-2
13,400円
Contents
1. Notation/ 2. Vectors, Vector Spaces, and Convexity/ 3. Rank/ 4. Matrix Functions: Inverse, Transpose, Trace, Determinant, and Norm/ 5. Complex, Hermitian, and Related Matrices/ 6. Eigenvalues, Eigenvectors, and Singular Values/ 7. Generalized Inverses/ 8. Some Special Matrices/ 9. Non-Negative Vectors and Matrices/ 10. Positive Definite and Non-negative Definite Matrices/ 11. Special Products and Operators/ 12. Inequalities/ 13. Linear Equations/ 14. Partitioned Matrices/ 15. Patterned Matrices/ 16. Factorization of Matrices/ 17. Differentiation and Finite Differences/ 18. Jacobians/ 19. Matrix Limits, Sequences and Series/ 20. Random Vectors/ 21. Random Matrices/ 22. Inequalities for Probabilities and Random Variables/ 23. Majorization/ 24. Optimization and Matrix Approximation/ References/ Index/
* This book emphasizes computational statistics and algorithms and includes numerous references to both the theory behind the methods and the applications of the methods. Each chapter consists of four parts: a definition followed by a list of results, a short list of references to related topics in the book (since some overlap is unavoidable), one or more references to proofs, and references to applications. *
Rencher, A. C. & Schaalje,B. G.
Linear Models in Statistics 2nd ed.
統計学の線形モデル 第2版
John Wiley & Sons 2008.1
672 pp.(H)
ISBN 0-471-75498-6
14,600円
Contents
1. Introduction/ 2. Matrix Algebra/ 3. Random Vectors and Matrices/ 4. Multivariate Normal Distribution/ 5. Distribution of Quadratic Forms in y/ 6. Simple Linear Regression/ 7. Multiple Regression: Estimation/ 8. Multiple Regression: tests of Hypotheses and Confidence Intervals/ 9. Multiple Regression: Model Validation and Diagnostics/ 10. Multiple Regression: random x's/ 11. Multiple Regression: Bayesian Inference/ 12. Analysis-of-Variance Models/ 13. One-Way Analysis-of-Variance: balanced Case/ 14. Two-Way Analysis-of Variance: Balanced Case/ 15. Analysis-of-Variance: The Cell Means Model for Unbalanced Data/ 16. Analysis-of-Covariance/ 17. Linear Mixed Models/ 18. Additional Models/ Appendix A. Answers and Hits to the Problems/ References/ Index/
* Linear Models in Statistics, Second Edition includes full coverage of advanced topics, such as mixed and generalized linear models, Bayesian linear models, two-way models with empty cells, geometry of least squares, vector-matrix calculus, simultaneous inference, and logistic and nonlinear regression. Algebraic, geometrical, frequentist, and Bayesian approaches to both the inference of linear models and the analysis of variance are also illustrated. Through the expansion of relevant material and the inclusion of the latest technological developments in the field, this book provides readers with the theoretical foundation to correctly interpret computer software output as well as effectively use, customize, and understand linear models. *
Longford, N. T.
Studying Human Populations
An Advanced Course in Statistics
Springer-Verlag 2007.12
472 pp.(H)
ISBN 0-387-98735-5
10,500円
Contents
1. ANOVA and Ordinary Regression/ 2. Maximum Likelihood Estimation/ 3. Sampling Methods/ 4. The Bayesian Paradigm/ 5. Incomplete Data/ 6. Imperfect Measurement / 7. Experiments and Observational Studies/ 8. Clinical Trials/ 9. Random Coefficients/ 10. Generalised Linear Models/ 11. Longitudinal and Time-Series Analysis/ 12. Meta-Analysis and Estimating Many Quantities/ Appendix. A Refresher: A.1 Populations and Variables/ A.2 Replications and Randomness/ A.3 Notation/ A.4 Distributions/ A.5 Sampling Design/ A.6 Measurement Processes/ A.7 Infinite Populations/ A.8 Distributions/ A.9 Classes of Distributions and Models/ A.10 Normal Distributions/ A.11 Uniform Distributions/ A.12 Beta and Gamma Distributions/ A.13 Classes of Discrete Distributions/ A.14 Discrete Bivariate Distributions/ A.15 Bivariate Continuous Distributions/ A.16 Operating with Bivariate Distributions/ A.17Random Samples/ A.18Regression/ A.19 Multivariate Distributions/ A.20 Formulating Inferences/ References/ Index/ *
962-11 登録日 08.03.16
