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Foundations of Agnostic Statistics

Foundations of Agnostic Statistics


  • Date Published: January 2019
  • availability: In stock
  • format: Paperback
  • isbn: 9781316631140

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About the Authors
  • 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.

    • Provides a rigorous and targeted mathematical introduction to the statistics underlying modern statistical methodology in the social and health sciences
    • Prepares readers to go on to advanced study in statistical methodology, including in causal inference, nonparametric statistics, and econometrics
    • Develops the fundamentals of 'agnostic statistics' - an approach that asks what can be learned about the world under minimal assumptions
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    Product details

    • Date Published: January 2019
    • format: Paperback
    • isbn: 9781316631140
    • length: 314 pages
    • dimensions: 228 x 151 x 14 mm
    • weight: 0.52kg
    • contains: 35 b/w illus.
    • availability: In stock
  • Table of Contents

    Part I. Probability:
    1. Probability theory
    2. Summarizing distributions
    Part II. Statistics:
    3. Learning from random samples
    4. Regression
    5. Parametric models
    Part III. Identification:
    6. Missing data
    7. Causal inference.

  • Authors

    Peter M. Aronow, Yale University, Connecticut
    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, Yale University, Connecticut
    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.

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