“My ideas of romance came from the movies,” said Woody Allen, and it is to the movies—as well as to novels, advice columns, and self-help books—that David Shumway turns for his history of modern love. Modern Love argues that a crisis in the meaning and experience of marriage emerged when it lost its institutional function of controlling the distribution of property, and instead came to be seen as a locus for feelings of desire, togetherness, and loss. Over the course of the twentieth century, partly in response to this crisis, a new language of love—“intimacy”—emerged, not so much replacing but rather coexisting with the earlier language of “romance.” Reading a wide range of texts, from early twentieth-century advice columns and their late twentieth-century antecedent, the relationship self-help book, to Hollywood screwball comedies, and from the “relationship films” of Woody Allen and his successors to contemporary realist novels about marriages, Shumway argues that the kinds of stories the culture has told itself have changed. Part layperson’s history of marriage and romance, part meditation on intimacy itself, Modern Love will be both amusing and interesting to almost anyone who thinks about relationships (and who doesn’t?).
This genealogy begins around 1890, when American literature as defined by institutions outside the academy, such as magazines and publishing houses, acquired much of the ideology it would display in later phases, including sexism, racism, and class bias.
Ever since their arrival in North America, European colonists and their descendants have struggled to explain the epidemics that decimated native populations. Jones examines crucial episodes in this history, from Puritan responses to Indian depopulation to programs to test new antibiotics and implement modern medicine on the Navajo reservation.
John Sayles is the very paradigm of the contemporary independent filmmaker. By raising much of the funding for his films himself, Sayles functions more independently than most directors, and he has used his freedom to write and produce films with a distinctive personal style and often clearly expressed political positions. From The Return of the Secaucus Seven to Sunshine State, his films have consistently expressed progressive political positions on issues including race, gender, sexuality, class, and disability. In this study, David R. Shumway examines the defining characteristic of Sayles's cinema: its realism. Positing the filmmaker as a critical realist, Shumway explores Sayles's attention to narrative in critically acclaimed and popular films such as Matewan, Eight Men Out, Passion Fish, and Lone Star. The study also details the conditions under which Sayles's films have been produced, distributed, and exhibited, affecting the way in which these films have been understood and appreciated. In the process, Shumway presents Sayles as a teacher who tells historically accurate stories that invite audiences to consider the human world they all inhabit.
A balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using non-trivial data illustrate solutions to problems, such as evaluating pain perception experiments using magnetic resonance imaging or monitoring a nuclear test ban treaty. Although designed as a text for graduate level students in statistics and the physical, biological and social sciences, some parts of the book will also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels, and the material has been updated by adding modern developments involving categorical time series analysis and the spectral envelope, multivariate spectral methods, long memory series, nonlinear models, longitudinal data analysis, resampling techniques, ARCH models, stochastic volatility, wavelets and Monte Carlo Markov chain integration methods. The book is supplemented by data and an exploratory time series analysis program ASTSA for Windows that can be downloaded from the Web as freeware.
The goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis. Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the high school level. All of the numerical examples use the R statistical package without assuming that the reader has previously used the software. Robert H. Shumway is Professor Emeritus of Statistics, University of California, Davis. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is the author of numerous texts and served on editorial boards such as the Journal of Forecasting and the Journal of the American Statistical Association. David S. Stoffer is Professor of Statistics, University of Pittsburgh. He is a Fellow of the American Statistical Association and has won the American Statistical Association Award for Outstanding Statistical Application. He is currently on the editorial boards of the Journal of Forecasting, the Annals of Statistical Mathematics, and the Journal of Time Series Analysis. He served as a Program Director in the Division of Mathematical Sciences at the National Science Foundation and as an Associate Editor for the Journal of the American Statistical Association and the Journal of Business & Economic Statistics.
The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.
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