Foundations of Agnostic Statistics (Paperback)

Peter M. Aronow

ISBN 10: 1316631141 / ISBN 13: 9781316631140

Published by Cambridge University Press, Cambridge, 2019

About this Item

Paperback. Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge. This book provides a rigorous but accessible treatment of modern statistical methodology for researchers in the social and health sciences. It provides readers with the mathematical tools to critically engage with cutting-edge statistical methods. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9781316631140

Bibliographic Details

• Title: Foundations of Agnostic Statistics (Paperback)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2019
• Language: English
• ISBN 10: 1316631141
• ISBN 13: 9781316631140
• Binding: Paperback
• Condition: new

About this title

Synopsis

Reflecting a sea change in how empirical research has been conducted over the past three decades, Foundations of Agnostic Statistics presents an innovative treatment of modern statistical theory for the social and health sciences. This book develops the fundamentals of what the authors call agnostic statistics, which considers what can be learned about the world without assuming that there exists a simple generative model that can be known to be true. Aronow and Miller provide the foundations for statistical inference for researchers unwilling to make assumptions beyond what they or their audience would find credible. Building from first principles, the book covers topics including estimation theory, regression, maximum likelihood, missing data, and causal inference. Using these principles, readers will be able to formally articulate their targets of inquiry, distinguish substantive assumptions from statistical assumptions, and ultimately engage in cutting-edge quantitative empirical research that contributes to human knowledge.

About the Author

Peter M. Aronow is an Associate Professor of Political Science, Public Health (Biostatistics), and Statistics and Data Science at Yale University, Connecticut and is affiliated with the University's Institution for Social and Policy Studies, Center for the Study of American Politics, Institute for Network Science, and Operations Research Doctoral Program.

Benjamin T. Miller is a doctoral candidate in Political Science at Yale University, Connecticut. In 2012, Mr Miller received a B.A. in Economics and Mathematics from Amherst College.