Description
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.
Each of the two volumes is divided into three parts, with each part preceded by an introduction, summarizing the main developments in the areas covered in that part. Volume 1 deals with methods of sample selection and data processing, with the later including editing and imputation, handling of outliers and measurement errors, and methods of disclosure control. The volume contains also a large variety of applications in specialized areas such as household and business surveys, marketing research, opinion polls and censuses. Volume 2 is concerned with inference, distinguishing between design-based and model-based methods and focusing on specific problems such as small area estimation, analysis of longitudinal data, categorical data analysis and inference on distribution functions. The volume contains also chapters dealing with case-control studies, asymptotic properties of estimators and decision theoretic aspects.
Part 1. Sampling and Survey Design
Introduction to Part 1
Ch. 1. Introduction to Survey Sampling
Ch. 2. Sampling with Unequal Probabilities
Ch. 3. Two-Phase Sampling
Ch. 4. Multiple-Frame Surveys
Ch. 5. Designs for Surveys over Time
Ch. 6. Sampling of Rare Populations
Ch. 7. Design, Conduct, and Analysis of Random-Digit Dialing Surveys
Part 2. Survey Processing
Introduction to Part 2
Ch. 8. Nonresponse andWeighting
Ch. 9. Statistical Data Editing
Ch. 10. Imputation and Inference in the Presence of Missing Data
Ch. 11. Dealing with Outliers in Survey Data
Ch. 12. Measurement Errors in Sample Surveys
Ch. 13. Computer Software for Sample Surveys
Ch. 14. Record Linkage
Ch. 15. Statistical Disclosure Control for Survey Data
Part 3. Survey Applications
Introduction to Part 3
Ch. 16. Sampling and Estimation in Household Surveys
Ch. 17. Sampling and Estimation in Business Surveys
Ch. 18. Sampling, Data Collection, and Estimation in Agricultural Surveys
Ch. 19. Sampling and Inference in Environmental Surveys
Ch. 20. Survey Sampling Methods in Marketing Research: A Review of Telephone,
Mall Intercept, Panel, andWeb Surveys
Ch. 21. Sample Surveys and Censuses
Ch. 22. Opinion and Election Polls
Volume 29B: Inference and Analysis
Part 4. Alternative Approaches to Inference from
Introduction to Part 4
Ch. 23. Model-Based Prediction of Finite Population Totals
Ch. 24. Design- and Model-Based Inference for Model Parameters
Ch. 25. CalibrationWeighting: Combining Probability Samples and Linear Prediction Models
Ch. 26. Estimating Functions and Survey Sampling
Ch. 27. Nonparametric and Semiparametric Estimation in Complex Surveys
Ch. 28. Resampling Methods in Surveys
Ch. 29. Bayesian Developments in Survey Sampling
Ch. 30. Empirical Likelihood Methods
Part 5. Special Estimation and Inference Problems
Ch. 31. Design-based Methods of Estimation for Domains and Small Areas
Ch. 32. Model-Based Approach to Small Area Estimation
Ch. 33. Design and Analysis of Surveys Repeated over Time
Ch. 34. The Analysis of Longitudinal Surveys
Ch. 35. Categorical Data Analysis for Simple and Complex Surveys
Ch. 36. Inference on Distribution Functions and Quantiles
Ch. 37. Scatterplots with Survey Data
Part 6. Informative Sampling and Theoretical Aspects
Ch. 38. Population-Based Case-Control Studies
Ch. 39. Inference under Informative Sampling
Ch. 40. Asymptotics in Finite Population Sampling
Ch. 41. Some Decision-Theoretic Aspects of Finite Population Sampling
Bibliographic details
Hardbound, 666 pages, publication date: SEP-2009
ISBN-13: 978-0-444-53438-5
Paperback (ISBN-13: 9780521119917)
Cryptography is concerned with the conceptualization, definition and construction of computing systems that address security concerns. The design of cryptographic systems must be based on firm foundations. Foundations of Cryptography presents a rigorous and systematic treatment of foundational issues, defining cryptographic tasks and solving cryptographic problems. The emphasis is on the clarification of fundamental concepts and on demonstrating the feasibility of solving several central cryptographic problems, as opposed to describing ad-hoc approaches. This second volume contains a thorough treatment of three basic applications: Encryption, Signatures, and General Cryptographic Protocols. It builds on the previous volume, which provided a treatment of one-way functions, pseudorandomness, and zero-knowledge proofs. It is suitable for use in a graduate course on cryptography and as a reference book for experts. The author assumes basic familiarity with the design and analysis of algorithms; some knowledge of complexity theory and probability is also useful.
* Rigorous and systematic treatment of cryptography, focused on concepts
and ideas * Lots of exercises and examples * Suitable for experts as well
as beginners who have a background in theory of computation
List of figures; Preface; Acknowledgements; 5. Encryption schemes; 6. Digital signatures and message authentication; 7. General cryptographic protocols; Appendix C: corrections and additions to volume I; Bibliography; Index.
Hardback (ISBN-13: 9780521514972)
Paperback (ISBN-13: 9780521735612)
General relativity is now an essential part of undergraduate and graduate courses in physics, astrophysics and applied mathematics. This simple, user-friendly introduction to relativity is ideal for a first course in the subject. Beginning with a comprehensive but simple review of special relativity, the book creates a framework from which to launch the ideas of general relativity. After describing the basic theory, it moves on to describe important applications to astrophysics, black hole physics, and cosmology. Several worked examples, and numerous figures and images, help students appreciate the underlying concepts. There are also 180 exercises which test and develop studentsf understanding of the subject. The textbook presents all the necessary information and discussion for an elementary approach to relativity. Password-protected solutions to the exercises are available to instructors at www.cambridge.org/9780521735612.
* Devoted to general relativity, it is designed to be a simple introduction
to the topic for undergraduates and graduates * 180 exercises, several
worked examples and figures help students to test and develop their understanding
of the subject * Solutions to all exercises are available to lecturers
online to aid with homework setting and class preparation
1. The special theory of relativity; 2. From special to the general theory of relativity; 3. Vectors and tensors; 4. Covariant differentiation; 5. Curvature of spacetime; 6. Spacetime symmetries; 7. Physics in curved spacetime; 8. Einstein's equations; 9. The Schwarzschild solutions; 10. Experimental tests of general relativity; 11. Gravitational radiation; 12. Relativistic astrophysics; 13. Black holes; 14. The expanding universe; 15. Friedmann models; 16. The early universe; 17. Observational cosmology; 18. Beyond relativity; References; Index.
EMS Textbooks in Mathematics
ISBN 978-3-03719-071-5
August 2009, 368 pages, hardcover, 16.5 x 23.5 cm.
Markov chains are the first and most important examples of random processes. This book is about time-homogeneous Markov chains that evolve with discrete time steps on a countable state space. Measure theory is not avoided, careful and complete proofs are provided.
A specific feature is the systematic use, on a relatively elementary level,
of generating functions associated with transition probabilities for analyzing
Markov chains. Basic definitions and facts include the construction of
the trajectory space and are followed by ample material concerning recurrence
and transience, the convergence and ergodic theorems for positive recurrent
chains. There is a side-trip to the Perron*Frobenius theorem. Special attention
is given to reversible Markov chains and to basic mathematical models of
gpopulation evolutionh such as birth-and-death chains, Galton*Watson
process and branching Markov chains.
A good part of the second half is devoted to the introduction of the basic language and elements of the potential theory of transient Markov chains. Here the construction and properties of the Martin boundary for describing positive harmonic functions are crucial. In the long final chapter on nearest neighbour random walks on (typically infinite) trees the reader can harvest from the seed of methods laid out so far, in order to obtain a rather detailed understanding of a specific, broad class of Markov chains.
The level varies from basic to more advanced, addressing an audience from masterfs degree students to researchers in mathematics, and persons who want to teach the subject on a medium or advanced level. A specific characteristic of the book is the rich source of classroom-tested exercises with solutions.
ISBN: 978-0-470-01154-6
Hardcover
600 pages
December 2009
Bayesian methods are increasingly being used in the social sciences, as
the problems encountered lend themselves so naturally to the subjective
qualities of Bayesian methodology. This book provides an accessible introduction
to Bayesian methods, tailored specifically for social science students.
It contains lots of real examples from political science, psychology, sociology,
and economics, exercises in all chapters, and detailed descriptions of
all the key concepts, without assuming any background in statistics beyond
a first course. It features examples of how to implement the methods using
WinBUGS * the most-widely used Bayesian analysis software in the world
* and R * an open-source statistical software. The book is supported by
a Website featuring WinBUGS and R code, data sets, and solutions to exercises.
List of Figures
List of Tables
Preface
Acknowledgments
Introduction
Part I Introducing Bayesian Analysis
1 The foundations of Bayesian inference
1.1 What is probability*
1.2 Subjective probability in Bayesian statistics
1.3 Bayes theorem, discrete case
1.4 Bayes theorem, continuous parameter
1.5 Parameters as random variables, beliefs as distributions
1.6 Communicating the results of a Bayesian analysis
1.7 Asymptotic properties of posterior distributions
1.8 Bayesian hypothesis testing
1.9 From subjective beliefs to parameters and models
1.10 Historical note
2 Getting started: Bayesian analysis for simple models
2.1 Learning about probabilities, rates and proportions
2.2 Associations between binary variables
2.3 Learning from counts
2.4 Learning about a normal mean and variance
2.5 Regression models
2.6 Further reading
Part II Simulation Based Bayesian Analysis
3 Monte Carlo methods
3.1 Simulation consistency
3.2 Inference for functions of parameters
3.3 Marginalization via Monte Carlo integration
3.4 Sampling algorithms
3.5 Further reading
4 Markov chains
4.1 Notation and definitions
4.2 Properties of Markov chains
4.3 Convergence of Markov chains
4.4 Limit theorems for Markov chains
4.5 Further reading
5 Markov chain Monte Carlo
5.1 Metropolis-Hastings algorithm
5.2 Gibbs sampling
6 Implementing Markov chain Monte Carlo
6.1 Software for Markov chain Monte Carlo
6.2 Assessing convergence and run-length
6.3 Working with BUGS/JAGS from R
6.4 Tricks of the trade
6.5 Other examples
6.6 Further reading
Part III Advanced Applications in the Social Sciences
7 Hierarchical Statistical Models
7.1 Data and parameters that vary by groups: the case for hierarchical modeling
7.2 ANOVA as a hierarchical model
7.3 Hierarchical models for longitudinal data
7.4 Hierarchical models for non-normal data
7.5 Multi-level models
8 Bayesian analysis of choice making
8.1 Regression models for binary responses
8.2 Ordered outcomes
8.3 Multinomial outcomes
8.4 Multinomial probit
9 Bayesian approaches to measurement
9.1 Bayesian inference for latent states
9.2 Factor analysis
9.3 Item-response models
9.4 Dynamic measurement models
Part IV Appendices
Appendix A: Working with vectors and matrices
Appendix B: Probability review
B.1 Foundations of probability
B.2 Probability densities and mass functions
B.3 Convergence of sequences of random variabales
Appendix C: Proofs of selected propositions
C.1 Products of normal densities
C.2 Conjugate analysis of normal data
C.3 Asymptotic normality of the posterior density
Topic index
Author index