Series: Statistics for Social and Behavioral Sciences
2007, Approx. 390 p., Hardcover
ISBN: 978-0-387-71264-2
Due: July 2007
About this book
Introduction to Applied Bayesian Statistics and Estimation for Social Scientists covers the complete process of Bayesian statistical analysis in great detail from the development of a model through the process of making statistical inference. The key feature of this book is that it covers models that are most commonly used in social science research, including the linear regression model, generalized linear models, hierarchical models, and multivariate regression models, and it thoroughly develops each real-data example in painstaking detail.
The first part of the book provides a detailed introduction to mathematical statistics and the Bayesian approach to statistics, as well as a thorough explanation of the rationale for using simulation methods to construct summaries of posterior distributions. Markov chain Monte Carlo (MCMC) methods?including the Gibbs sampler and the Metropolis-Hastings algorithm?are then introduced as general methods for simulating samples from distributions. Extensive discussion of programming MCMC algorithms, monitoring their performance, and improving them is provided before turning to the larger examples involving real social science models and data.
Scott M. Lynch is an associate professor in the Department of Sociology and Office of Population Research at Princeton University. His substantive research interests are in changes in racial and socioeconomic inequalities in health and mortality across age and time. His methodological interests are in the use of Bayesian stastistics in sociology and demography generally and in multistate life table methodology specifically.
Table of contentsIntroduction.- Probability theory and classical statistics.- Basics of Bayesian statistics.- Modern model estimation part 1: Gibbs sampling.- Modern model estimation part 2: Metroplis-Hastings sampling.- Evaluating MCMC algorithms and model fit.- The linear regression model.- Generalized linear models.- Introduction to hierarchical models.- Introduction to multivariate regression models.- Conclusion.
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2007, X, 558 p., Softcover
ISBN: 978-0-387-72805-6
Due: July 2007
About this book
"At last, after a decade of mounting interest in log-linear and related models for the analysis of discrete multivariate data, particularly in the form of multidimensional tables, we now have a comprehensive text and general reference on the subject. Even a mediocre attempt to organize the extensive and widely scattered literature on discrete multivariate analysis would be welcome; happily, this is an excellent such effort, but a group of Harvard statisticians taht has contributed much to the field. Their book ought to serve as a basic guide to the analysis of quantitative data for years to come." --James R. Beninger, Contemporary Sociology
"A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature
"Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level, except in Chapters 12 and 14, is very elementary. To illustrate possible applications, some 60 different sets of data have been gathered together from diverse fields. To aid the reader, an index of these examples has been provided. ...the book contains a wealth of material on important topics. Its numerous examples are especially valuable." --Shelby J. Haberman, The Annals of Statistics
Table of contents
Introduction.- Structural models for counted data.- Maximum likelihood estimates for complete tables.- Formal goodness of fit: Summary statistics and model selection.- Maximum likelihood estimation for incomplete tables.- Estimating the size of a closed population.- Models for measuring change.- Analysis of square tables: Symmetry and marginal homogeneity.- Model selection and assessing closeness of fit: Practical aspects.- Other methods for estimation and testing in cross-classifications.- Measures of association and agreement.- Pseudo-Bayes estimates of cell probabilites.- Sampling models for discrete data.- Asymptotic methods.
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Based on the 6th German edition "Taschenbuch der Mathematik", published by Wissenschaftlicher Verlag Harri Deutsch, Frankfurt/M., 2005
5th ed., 2007, XLIV, 1163 p., 745 illus., Softcover
ISBN: 978-3-540-72121-5
Due: July 23, 2007
About this book
Fifth edition has been fundamentally revised, updated and expanded
Reference book providing rapid access to formulae, data, and concepts of applied mathematics
Famous and classic reference book for engineers, physicists, and applied mathematicians worldwide
Includes more than 700 illustrations as well as 142 tables
This guide book to mathematics contains in handbook form the fundamental working knowledge of mathematics which is needed as an everyday guide for working scientists and engineers, as well as for students. Easy to understand, and convenient to use, this guide book gives concisely the information necessary to evaluate most problems which occur in concrete applications. In the newer editions emphasis was laid on those fields of mathematics that became more important for the formulation and modeling of technical and natural processes, namely Numerical Mathematics, Probability Theory and Statistics, as well as Information Processing. For the 5th edition, the chapters "Computer Algebra Systems" and "Dynamical Systems and Chaos" were fundamentally revised, updated and expanded. In the chapter "Algebra and Discrete Mathematics" a section on "Finite Fields and Shift Registers" was added.
Table of contents
Arithmetic.- Functions.- Geometry.- Linear Algebra.- Algebra and Discrete Mathematics.- Differentiation.- Infinite Series.- Integral Calculus.- Differential Equations.- Calculus of Variations.- Linear Integral Equations.- Functional Analysis.- Vector Analysis and Vector Fields.- Functional Theory.- Integral Transformations.- Probability Theory and Mathematical Statistics.- Dynamical Systems and Chaos.- Optimization.- Numerical Analysis.- Computer Algebra Systems.- Tables.