This expanded edition includes new data and easy-to-read graphics explaining the 2008 election. Red State, Blue State, Rich State, Poor State is a must-read for anyone seeking to make sense of today's fractured political landscape.
This updated edition of Gorn's highly influential history of the early prize rings features a new afterword, the author's meditation on the ways in which studies of sport, gender, and popular culture have changed in the quarter century since the book was first published. An up-to-date bibliography ensures that The Manly Art will remain a vital resource for a new generation.
Surveying critical episodes in the development of American political parties—from their formation in the 1790s to the Civil War—Aldrich shows how they serve to combat three fundamental problems of democracy: how to regulate the number of people seeking public office, how to mobilize voters, and how to achieve and maintain the majorities needed to accomplish goals once in office. Aldrich brings this innovative account up to the present by looking at the profound changes in the character of political parties since World War II, especially in light of ongoing contemporary transformations, including the rise of the Republican Party in the South, and what those changes accomplish, such as the Obama Health Care plan. Finally, Why Parties? A Second Look offers a fuller consideration of party systems in general, especially the two-party system in the United States, and explains why this system is necessary for effective democracy.
Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors—all leaders in the statistics community—introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice.
New to the Third Edition
Four new chapters on nonparametric modeling Coverage of weakly informative priors and boundary-avoiding priors Updated discussion of cross-validation and predictive information criteria Improved convergence monitoring and effective sample size calculations for iterative simulation Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation New and revised software code
The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.
Key features of the book include:Comprehensive coverage of an imporant area for both research and applications. Adopts a pragmatic approach to describing a wide range of intermediate and advanced statistical techniques. Covers key topics such as multiple imputation, propensity scores, instrumental variables and Bayesian inference. Includes a number of applications from the social and health sciences. Edited and authored by highly respected researchers in the area.
The Handbook of Markov Chain Monte Carlo provides a reference for the broad audience of developers and users of MCMC methodology interested in keeping up with cutting-edge theory and applications. The first half of the book covers MCMC foundations, methodology, and algorithms. The second half considers the use of MCMC in a variety of practical applications including in educational research, astrophysics, brain imaging, ecology, and sociology.
The in-depth introductory section of the book allows graduate students and practicing scientists new to MCMC to become thoroughly acquainted with the basic theory, algorithms, and applications. The book supplies detailed examples and case studies of realistic scientific problems presenting the diversity of methods used by the wide-ranging MCMC community. Those familiar with MCMC methods will find this book a useful refresher of current theory and recent developments.