1.

Record Nr.

UNINA9910460564703321

Titolo

Applying anthropology to gender-based violence : global responses, local practices / / edited by Jennifer R. Wies and Hillary J. Haldane

Pubbl/distr/stampa

Lanham [Maryland] : , : Lexington Books, , [2015]

©2015

ISBN

1-4985-0904-5

Descrizione fisica

1 online resource (228 p.)

Disciplina

303.6

Soggetti

Violence

Women - Violence against

Abused women

Family violence

Electronic books.

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Return to the local : lessons for global change / Jennifer R. Wies and Hillary J. Haldane -- Domestic violence, embodiment, and women's lives in northern Vietnam / Lynn Kwiatkowski -- Bureaucratic bindings : refugee resettlement and intimate partner abuse / Elizabeth Wirtz -- Munted : rebuilding community after disaster / Hillary J. Haldane -- Gender-based violence and the state in Guatemala's genocide and beyond / M. Gabriela Torres -- Prostitution diversion programs structural violence / Yasmina Katsulis -- Sex trafficking of native peoples : history, race, and law / April D.J. Petillo -- Pa manyen fanm nan konsa : understanding violence against women after Haiti's earthquake / Mark Schuller -- Campus sexual violence policies and practices : a holistic and historical approach to research and practice / Jennifer R. Wies -- "I'm a real father now!" : using applied anthropology to promote positive masculinities to reduce family violence in northern Uganda / Rebecka Lundgren and Kimberly Ashburn -- Employing scholar-activist anthropology to counter gender-based violence in Belize / Melissa Beske -- Intimate partner violence, social change, and scholar-activism in coastal Ecuador / Karin Friederic.



Sommario/riassunto

Applying Anthropology to Gender-Based Violence emphasizes the strength of an applied anthropology and ethnographic approach to ending gender-based violence worldwide. This book sets an activist and engaged agenda for scholars and students to follow as they work to blend passion, theory, and methods in their efforts to end violence.

2.

Record Nr.

UNINA9910777458303321

Autore

Berk Richard A

Titolo

Statistical learning from a regression perspective [[electronic resource] /] / Richard A. Berk

Pubbl/distr/stampa

New York, : Springer, 2008

ISBN

1-281-49137-3

9786611491376

0-387-77501-3

Edizione

[1st ed. 2008.]

Descrizione fisica

1 online resource (377 p.)

Collana

Springer series in statistics

Disciplina

519.5/36

Soggetti

Regression analysis

Lingua di pubblicazione

Inglese

Formato

Materiale a stampa

Livello bibliografico

Monografia

Note generali

Description based upon print version of record.

Nota di bibliografia

Includes bibliographical references and index.

Nota di contenuto

Statistical Learning as a Regression Problem -- Regression Splines and Regression Smoothers -- Classification and Regression Trees (CART) -- Bagging -- Random Forests -- Boosting -- Support Vector Machines -- Broader Implications and a Bit of Craft Lore.

Sommario/riassunto

Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical. Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example,



in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide. The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R. Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences.