Generalized Linear Models: A Unified Approach (Quantitative Applications in the Social Sciences)

Gill, Jefferson M.,Torres Pacheco, Silvia Michelle

ISBN 10: 1506387349 / ISBN 13: 9781506387345

Editore: SAGE Publications, Inc, 2019

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Codice articolo S_437089532

Dati bibliografici

• Titolo: Generalized Linear Models: A Unified Approach (Quantitative Applications in the Social Sciences)
• Editore: SAGE Publications, Inc
• Data di pubblicazione: 2019
• Lingua: Inglese
• ISBN 10: 1506387349
• ISBN 13: 9781506387345
• Rilegatura: paperback
• Edizione: seconda edizione
• Condizione: Good

Descrizione articolo

Riassunto

Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. Authors Jeff Gill and Michelle Torres provide examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on the multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.

Informazioni sugli autori

Jeff Gill is a Distinguished Professor of Government, a Professor of Statistics, and a Member of the Center for Behavioral Neuroscience at American University. His research applies Bayesian modeling and data analysis (decision theory, testing, model selection, elicited priors) to questions in general social science quantitative methodology, political behavior and institutions, medical/health data analysis especially physiology, circulation/blood, pediatric traumatic brain injury, and epidemiological measurement/data issues, using computationally intensive tools (Monte Carlo methods, MCMC, stochastic optimization, nonparametrics).

Michelle Torres is Assistant professor in the Department of Political Science at Rice University. She holds a PhD in Political Science and a AM in Statistics from Washington University in St. Louis. Her research interests are in the fields of political methodology, with a special focus on survey methodology, computer vision, causal inference, public opinion, and political communication.