Gentle, J. E.
Computational Statistics
Springer-Verlag 2009.8
752 pp.(H)
ISBN 0-387-98143-8
12,700円
Contents
1. Mathematical and statistical preliminaries/ 2. Computer storage and arithmetic/ 3. Algorithms and programming/ 4. Approximation of functions and numerical quadrature/ 5. Numerical linear algebra/ 6. Solution of nonlinear equations and optimization/ 7. Generation of random numbers/ 8. Graphical methods in computational statistics/ 9. Tools for identification of structure in data/ 10. Estimation of functions/ 11. Monte Carlo methods for statistical inference/ 12. Data randomization, partitioning, and augmentation/ 13. Bootstrap methods/ 14. Estimation of probability density functions using parametric models/ 15. Nonparametric estimation of probability density functions/ 16. Statistical learning and data mining/ 17. Statistical models of dependencies/ Index/
* Computational inference has taken its place alongside asymptotic inference and exact techniques in the standard collection of statistical methods. Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators and test statistics. This book describes computationally-intensive statistical methods in a unified presentation, emphasizing techniques, such as the PDF decomposition, that arise in a wide range of methods. *
Pfeffermann, D. & Rao, C. R.
Handbook of Statistics Vol.29B
Sampling Surveys: Inference and Analysis
Elsevier 2009.9
666 pp.(H)
ISBN 0-444-53438-5
28,600円
Contents
1. Sampling and Survey Design/ 2. Survey Processing/ 3. Survey Applications/ 4. Alternative Approaches to Inference from/ 5. Special Estimation and Inference Problems/ 6. Informative Sampling and Theoretical Aspects/ Index/
* This new handbook contains the most comprehensive account of sample surveys theory and practice to date. It is a second volume on sample surveys, with the goal of updating and extending the sampling volume published as volume 6 of the Handbook of Statistics in 1988. The present handbook is divided into two volumes (29A and 29B), with a total of 41 chapters, covering current developments in almost every aspect of sample surveys, with references to important contributions and available software. It can serve as a self contained guide to researchers and practitioners, with appropriate balance between theory and real life applications. *
Ross, S.
Introduction to Probability Models 10th ed.
確率モデル入門 第１０版
Academic Press 2009.12
800 pp.(H)
ISBN 0-12-375686-3
11,400円
Contents
1. Introduction to Probability Theory/ 2. Random Variables/ 3. Conditional Probability and Conditional Expectation/ 4. Markov Chains/ 5. The Exponential Distribution and the Poisson Process/ 6. Continuous-Time Markov Chains/ 7. Renewal Theory and Its Applications/ 8. Queueing Theory/ 9. Reliability Theory/ 10. Brownian Motion and Stationary Processes/ 11. Simulation/ Appendix: Solutions to Starred Exercises/ Index/
* Ross's classic bestseller, Introduction to Probability Models, has been used extensively by professionals and as the primary text for a first undergraduate course in applied probability. It provides an introduction to elementary probability theory and stochastic processes, and shows how probability theory can be applied to the study of phenomena in fields such as engineering, computer science, management science, the physical and social sciences, and operations research. With the addition of several new sections relating to actuaries, this text is highly recommended by the Society of Actuaries. *
Lumley, T. S.
Complex Surveys
A Guide to Analysis Using R
John Wiley & Sons 2010.3
276 pp.(P)
ISBN 0-470-28430-7
8,500円
Contents
1. Basic Tools/ 2. Simple and Stratified sampling/ 3. Cluster sampling/ 4. Graphics/ 5. Ratios and linear regression/ 6. Categorical data regression 109/ 7. Poststratification, raking and calibration/ 8. Twophase sampling/ 9. Missing data/ 10. Causal inference/ Appendix A: Analytic details/ Appendix B: Basic R/ Appendix C: Computational details/ Appendix D: Databasebacked design objects/ Appendix E: Extending the survey package/ References/ Index/
* The book begins with coverage of basic tools and topics within survey analysis such as simple and stratified sampling, cluster sampling, linear regression, and categorical data regression. Subsequent chapters delve into more technical aspects of complex survey analysis, including post-stratification, two-phase sampling, missing data, and causal inference. Throughout the book, an emphasis is placed on graphics, regression modeling, and two-phase designs. In addition, the author supplies a unique discussion of epidemiological two-phase designs as well as probability-weighting for causal inference. All of the book's examples and figures are generated using R, and a related Web site provides the R code that allows readers to reproduce the presented content. *
Fujikoshi, Y. et al.
Multivariate Statistics
High-Dimensional and Large-Sample Approximations
（多変量統計学）
John Wiley & Sons 2009.10
528 pp.(H)
ISBN 0-470-41169-4
13,400円
Contents
1. Multivariate Normal and Related Distributions/ 2. Wishart Distribution/ 3. T2- and Lambda-Statistics/ 4. Correlation Coefficients/ 5. Asymptotic Expansions for Multivariate Basic Statistics/ 6. MANOVA Models/ 7. Multivariate Regression/ 8. Classical and High-Dimensional Tests for Covariance Matrices/ 9. Discriminant Analysis/ 10. Principal Component Analysis/ 11. Canonical Correlation Analysis/ 12. Growth Curve Analysis/ 13. Theory of Approximation to the Distribution of Scale Mixture/ 14. Basic Theory of Approximation to Some Related Distributions/ 15. Error Bounds for Approximations of Some Multivariate Tests/ 16. Error Bounds for Approximations of Some Other Statistics/ A: Appendix: Some Results on Matrices/ Bibliography/ Index/
* Multivariate Statistics: High-Dimensional and Large-Sample Approximations is the first book of its kind to explore how classical multivariate methods can be revised and used in place of conventional statistical tools. Written by prominent researchers in the field, the book focuses on high-dimensional and large-scale approximations and details the many basic multivariate methods used to achieve high levels of accuracy. *
Heritier, S. et al.
Robust Methods in Biostatistics
(Series in Probability and Statistics)
John Wiley & Sons 2009.6
292 pp. (H)
ISBN 0-470-02726-6
12,800円
Contents
1. Introduction/ 2. Key Measures and Results/ 3. Linear Regression/ 4. Mixed Linear Models/ 5. Generalized Linear Models/ 6. Marginal Longitudinal Data Analysis/ 7. Survival Analysis/ Appendices/ References/ Index/ *
Robert, C. & Casella, G.
Introducing Monte Carlo Methods with R
Springer-Verlag 2009.12
284 pp.(P)
ISBN 1-4419-1575-3
9,400円
Contents
1. Basic R Programming/ 2. Random Variable Generation/ 3. Monte Carlo Integration/ 4. Controlling and Accelerating Convergence/ 5. Monte Carlo Optimization/ 6. Metropolis{Hastings Algorithms/ 7. Gibbs Samplers/ 8. Monitoring and Adaptation for MCMC Algorithms/ References/ Index/
* Computational techniques based on simulation have now become an essential part of the statistician's toolbox. It is thus crucial to provide statisticians with a practical understanding of those methods, and there is no better way to develop intuition and skills for simulation than to use simulation to solve statistical problems. Introducing Monte Carlo Methods with R covers the main tools used in statistical simulation from a programmer's point of view, explaining the R implementation of each simulation technique and providing the output for better understanding and comparison. *
Horowitz, J. L.
Semiparametric and Nonparametric Methods in Econometrics
Springer-Verlag 2009.8
276 pp.(H)
ISBN 0-387-92869-3
11,200円
Contents
1. Introduction/ 2. Single-Index Models/ 3. Nonparametric Additive Models and Semiparametric Partially Linear Models/ 4. Binary-Response Models/ 5. Statistical Inverse Problems/ 6. Transformation Models/ Appendix: Nonparametric Density Estimation and Nonparametric Regression/ References/ Index/
* Standard methods for estimating empirical models in economics and many other fields rely on strong assumptions about functional forms and the distributions of unobserved random variables. Often, it is assumed that functions of interest are linear or that unobserved random variables are normally distributed. Such assumptions simplify estimation and statistical inference but are rarely justified by economic theory or other a priori considerations. Inference based on convenient but incorrect assumptions about functional forms and distributions can be highly misleading. Nonparametric and semiparametric statistical methods provide a way to reduce the strength of the assumptions required for estimation and inference, thereby reducing the opportunities for obtaining misleading results. *
Manton, K.G. et ak.
Cancer Mortality and Morbidity Patterns in the U.S. Population
An Interdisciplinary Approach
(Statistics for Biology and Health Series)
Springer-Verlag 2009.
455 pp. (H)
ISBN 0-387-78192-7
11,400円
Contents
1. Introduction: Cancer contra human: cohabitation with casualties?/ 2. Cancer modeling: how far can we move?/ 3. Cancer risk factors/ 4. Standard and innovative statistical methods for empirically analyzing cancer morbidity and mortality/ 5. Stochastic methods of analysis/ 6. U.S. cancer morbidity and mortality risks/ 7. U.S. cancer morbidity: modeling age-patterns of cancer histotypes/ 8. Risk factors intervention/ 9. Cancer prevention/ 10. Conclusion/outlook/ Appendices/ Index/ *
677-68 登録日 10.04.17
